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An essential problem in causal inference is estimating causal effects from observational data. The problem becomes more challenging with the presence of unobserved confounders. When there are unobserved confounders, the commonly used back-door adjustment is not applicable. Although the instrumental variable (IV) methods can deal with unobserved confounders, they all assume that the treatment directly affects the outcome, and there is no mediator between the treatment and the outcome. This paper aims to use the front-door criterion to address the challenging problem with the presence of unobserved confounders and mediators. In practice, it is often difficult to identify the set of variables used for front-door adjustment from data. By leveraging the ability of deep generative models in representation learning, we propose FDVAE to learn the representation of a Front-Door adjustment set with a Variational AutoEncoder, instead of trying to search for a set of variables for front-door adjustment. Extensive experiments on synthetic datasets validate the effectiveness of FDVAE and its superiority over existing methods. The experiments also show that the performance of FDVAE is not sensitive to the causal strength of unobserved confounders and is feasible in the case of dimensionality mismatch between learned representations and the ground truth. We further apply the method to three real-world datasets to demonstrate its potential applications.

A predictive model makes outcome predictions based on some given features, i.e., it estimates the conditional probability of the outcome given a feature vector. In general, a predictive model cannot estimate the causal effect of a feature on the outcome, i.e., how the outcome will change if the feature is changed while keeping the values of other features unchanged. This is because causal effect estimation requires interventional probabilities. However, many real world problems such as personalised decision making, recommendation, and fairness computing, need to know the causal effect of any feature on the outcome for a given instance. This is different from the traditional causal effect estimation problem with a fixed treatment variable. This paper first tackles the challenge of estimating the causal effect of any feature (as the treatment) on the outcome w.r.t. a given instance. The theoretical results naturally link a predictive model to causal effect estimations and imply that a predictive model is causally interpretable when the conditions identified in the paper are satisfied. The paper also reveals the robust property of a causally interpretable model. We use experiments to demonstrate that various types of predictive models, when satisfying the conditions identified in this paper, can estimate the causal effects of features as accurately as state-of-the-art causal effect estimation methods. We also show the potential of such causally interpretable predictive models for robust predictions and personalised decision making., 16

Estimating direct and indirect causal effects from observational data is crucial to understanding the causal mechanisms and predicting the behaviour under different interventions. Causal mediation analysis is a method that is often used to reveal direct and indirect effects. Deep learning shows promise in mediation analysis, but the current methods only assume latent confounders that affect treatment, mediator and outcome simultaneously, and fail to identify different types of latent confounders (e.g., confounders that only affect the mediator or outcome). Furthermore, current methods are based on the sequential ignorability assumption, which is not feasible for dealing with multiple types of latent confounders. This work aims to circumvent the sequential ignorability assumption and applies the piecemeal deconfounding assumption as an alternative. We propose the Disentangled Mediation Analysis Variational AutoEncoder (DMAVAE), which disentangles the representations of latent confounders into three types to accurately estimate the natural direct effect, natural indirect effect and total effect. Experimental results show that the proposed method outperforms existing methods and has strong generalisation ability. We further apply the method to a real-world dataset to show its potential application.

One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data. However, causal effect estimation often suffers from the impacts of confounding bias caused by unmeasured confounders that affect both the treatment and the outcome. The instrumental variable (IV) approach is a powerful way to eliminate the confounding bias from latent confounders. However, the existing IV-based estimators require a nominated IV, and for a conditional IV (CIV) the corresponding conditioning set too, for causal effect estimation. This limits the application of IV-based estimators. In this paper, by leveraging the advantage of disentangled representation learning, we propose a novel method, named DVAE.CIV, for learning and disentangling the representations of CIV and the representations of its conditioning set for causal effect estimations from data with latent confounders. Extensive experimental results on both synthetic and real-world datasets demonstrate the superiority of the proposed DVAE.CIV method against the existing causal effect estimators., Comment: Debo Cheng and Ziqi Xu contributed equally. 20 pages, 5 tables, and 3 figures. Accepted at ECML-PKDD2023

The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders. A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. However, it is challenging to search for a standard IV from data directly due to the strict conditions. The conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables, allowing a wider choice of possible IVs and enabling broader practical applications of the IV approach. Nevertheless, there is not a data-driven method to discover a CIV and its conditioning set directly from data. To fill this gap, in this paper, we propose to learn the representations of the information of a CIV and its conditioning set from data with latent confounders for average causal effect estimation. By taking advantage of deep generative models, we develop a novel data-driven approach for simultaneously learning the representation of a CIV from measured variables and generating the representation of its conditioning set given measured variables. Extensive experiments on synthetic and real-world datasets show that our method outperforms the existing IV methods., 10 pages, 4 figures and 3 tables. Accepted by AAAI2023

In many fields of scientific research and real-world applications, unbiased estimation of causal effects from non-experimental data is crucial for understanding the mechanism underlying the data and for decision-making on effective responses or interventions. A great deal of research has been conducted on this challenging problem from different angles. For causal effect estimation in data, assumptions such as Markov property, faithfulness and causal sufficiency are always made. Under the assumptions, full knowledge such as, a set of covariates or an underlying causal graph, is still required. A practical challenge is that in many applications, no such full knowledge or only some partial knowledge is available. In recent years, research has emerged to use a search strategy based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promose in tackling the practical challenge. In this survey, we review the methods and focus on the challenges the data-driven methods face. We discuss the assumptions, strengths and limitations of the data-driven methods. We hope this review will motivate more researchers to design better data-driven methods based on graphical causal modelling for the challenging problem of causal effect estimation., Comment: 25 pages, 7 figures and 1 table

Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i.e., variables that affect both the treatment and the outcome. Unfortunately, this assumption is frequently violated in real-world applications, since some variables only affect the treatment but not the outcome, and vice versa. Moreover, in many cases only the proxy variables of the underlying confounding factors can be observed. In this work, we first show the importance of differentiating confounding factors from instrumental and risk factors for both average and conditional average treatment effect estimation, and then we propose a variational inference approach to simultaneously infer latent factors from the observed variables, disentangle the factors into three disjoint sets corresponding to the instrumental, confounding, and risk factors, and use the disentangled factors for treatment effect estimation. Experimental results demonstrate the effectiveness of the proposed method on a wide range of synthetic, benchmark, and real-world datasets., Published in the Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'21)

Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when there exist latent confounders. With the standard IV method, when a given IV is valid, unbiased estimation can be obtained, but the validity requirement of standard IV is strict and untestable. Conditional IV has been proposed to relax the requirement of standard IV by conditioning on a set of observed variables (known as a conditioning set for a conditional IV). However, the criterion for finding a conditioning set for a conditional IV needs complete causal structure knowledge or a directed acyclic graph (DAG) representing the causal relationships of both observed and unobserved variables. This makes it impossible to discover a conditioning set directly from data. In this paper, by leveraging maximal ancestral graphs (MAGs) in causal inference with latent variables, we propose a new type of IV, ancestral IV in MAG, and develop the theory to support data-driven discovery of the conditioning set for a given ancestral IV in MAG. Based on the theory, we develop an algorithm for unbiased causal effect estimation with an ancestral IV in MAG and observational data. Extensive experiments on synthetic and real-world datasets have demonstrated the performance of the algorithm in comparison with existing IV methods., 11 pages, 5 figures

Much research has been devoted to the problem of learning fair representations; however, they do not explicitly the relationship between latent representations. In many real-world applications, there may be causal relationships between latent representations. Furthermore, most fair representation learning methods focus on group-level fairness and are based on correlations, ignoring the causal relationships underlying the data. In this work, we theoretically demonstrate that using the structured representations enable downstream predictive models to achieve counterfactual fairness, and then we propose the Counterfactual Fairness Variational AutoEncoder (CF-VAE) to obtain structured representations with respect to domain knowledge. The experimental results show that the proposed method achieves better fairness and accuracy performance than the benchmark fairness methods., Comment: 9 pages, 3 figures, 4 tables

This paper studies the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model and the overall contribution of a feature to the model. The causal effect of a feature (variable) on the predicted outcome reflects the contribution of the feature to a prediction very well. A challenge is that most existing causal effects cannot be estimated from data without a known causal graph. In this paper, we define an explanatory causal effect based on a hypothetical ideal experiment. The definition brings several benefits to model agnostic explanations. First, explanations are transparent and have causal meanings. Second, the explanatory causal effect estimation can be data driven. Third, the causal effects provide both a local explanation for a specific prediction and a global explanation showing the overall importance of a feature in a predictive model. We further propose a method using individual and combined variables based on explanatory causal effects for explanations. We show the definition and the method work with experiments on some real-world data sets., Comment: 17

We study an interesting and challenging problem, learning any part of a Bayesian network (BN) structure. In this challenge, it will be computationally inefficient using existing global BN structure learning algorithms to find an entire BN structure to achieve the part of a BN structure in which we are interested. And local BN structure learning algorithms encounter the false edge orientation problem when they are directly used to tackle this challenging problem. In this paper, we first present a new concept of Expand-Backtracking to explain why local BN structure learning methods have the false edge orientation problem, then propose APSL, an efficient and accurate Any Part of BN Structure Learning algorithm. Specifically, APSL divides the V-structures in a Markov blanket (MB) into two types: collider V-structure and non-collider V-structure, then it starts from a node of interest and recursively finds both collider V-structures and non-collider V-structures in the found MBs, until the part of a BN structure in which we are interested are oriented. To improve the efficiency of APSL, we further design the APSL-FS algorithm using Feature Selection, APSL-FS. Using six benchmark BNs, the extensive experiments have validated the efficiency and accuracy of our methods.

Additional file 1. Supplementary tables, figures and note.

Additional file 1. Figure S1 and Tables S1 and S2.

Local-to-global learning approach plays an essential role in Bayesian network (BN) structure learning. Existing local-to-global learning algorithms first construct the skeleton of a DAG (directed acyclic graph) by learning the MB (Markov blanket) or PC (parents and children) of each variable in a data set, then orient edges in the skeleton. However, existing MB or PC learning methods are often computationally expensive especially with a large-sized BN, resulting in inefficient local-to-global learning algorithms. To tackle the problem, in this paper, we develop an efficient local-to-global learning approach using feature selection. Specifically, we first analyze the rationale of the well-known Minimum-Redundancy and Maximum-Relevance (MRMR) feature selection approach for learning a PC set of a variable. Based on the analysis, we propose an efficient F2SL (feature selection-based structure learning) approach to local-to-global BN structure learning. The F2SL approach first employs the MRMR approach to learn a DAG skeleton, then orients edges in the skeleton. Employing independence tests or score functions for orienting edges, we instantiate the F2SL approach into two new algorithms, F2SL-c (using independence tests) and F2SL-s (using score functions). Compared to the state-of-the-art local-to-global BN learning algorithms, the experiments validated that the proposed algorithms in this paper are more efficient and provide competitive structure learning quality than the compared algorithms.

Anomaly detection is an important research problem because anomalies often contain critical insights for understanding the unusual behavior in data. One type of anomaly detection approach is dependency-based, which identifies anomalies by examining the violations of the normal dependency among variables. These methods can discover subtle and meaningful anomalies with better interpretation. Existing dependency-based methods adopt different implementations and show different strengths and weaknesses. However, the theoretical fundamentals and the general process behind them have not been well studied. This paper proposes a general framework, DepAD, to provide a unified process for dependency-based anomaly detection. DepAD decomposes unsupervised anomaly detection tasks into feature selection and prediction problems. Utilizing off-the-shelf techniques, the DepAD framework can have various instantiations to suit different application domains. Comprehensive experiments have been conducted over one hundred instantiated DepAD methods with 32 real-world datasets to evaluate the performance of representative techniques in DepAD. To show the effectiveness of DepAD, we compare two DepAD methods with nine state-of-the-art anomaly detection methods, and the results show that DepAD methods outperform comparison methods in most cases. Through the DepAD framework, this paper gives guidance and inspiration for future research of dependency-based anomaly detection and provides a benchmark for its evaluation., 39 pages, 9 figures

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology., In this DREAM challenge, 75 methods for the identification of disease-relevant modules from molecular networks are compared and validated with GWAS data. The authors provide practical guidelines for users and establish benchmarks for network analysis.

The increasing application of machine learning techniques in everyday decision-making processes has brought concerns about the fairness of algorithmic decision-making. This paper concerns the problem of collider bias which produces spurious associations in fairness assessment and develops theorems to guide fairness assessment avoiding the collider bias. We consider a real-world application of auditing a trained classifier by an audit agency. We propose an unbiased assessment algorithm by utilising the developed theorems to reduce collider biases in the assessment. Experiments and simulations show the proposed algorithm reduces collider biases significantly in the assessment and is promising in auditing trained classifiers., 16 pages, 7 figures and 9 tables. The work has been accepted by PAKDD2022

Having a large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covariates is large relative to the samples available. Propensity score is a common way to deal with a large covariate set, but the accuracy of propensity score estimation (normally done by logistic regression) is also challenged by large number of covariates. In this paper, we prove that a large covariate set can be reduced to a lower dimensional representation which captures the complete information for adjustment in causal effect estimation. The theoretical result enables effective data-driven algorithms for causal effect estimation. We develop an algorithm which employs a supervised kernel dimension reduction method to search for a lower dimensional representation for the original covariates, and then utilizes nearest neighbor matching in the reduced covariate space to impute the counterfactual outcomes to avoid large-sized covariate set problem. The proposed algorithm is evaluated on two semi-synthetic and three real-world datasets and the results have demonstrated the effectiveness of the algorithm.

Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available. These methods either provide only a bound estimation of the causal effect of a treatment on the outcome, or generate a unique estimation of the causal effect, but making strong assumptions on data and having low efficiency. In this paper, we identify a practical problem setting and propose an approach to achieving unique and unbiased estimation of causal effects from data with hidden variables. For the approach, we have developed the theorems to support the discovery of the proper covariate sets for confounding adjustment (adjustment sets). Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation. Experiments with synthetic datasets generated using five benchmark Bayesian networks and four real-world datasets have demonstrated the efficiency and effectiveness of the proposed algorithms, indicating the practicability of the identified problem setting and the potential of the proposed approach in real-world applications., 12 pages,8 figures

Motivation: Uncovering the genomic causes of cancer, known as cancer driver genes, is a fundamental task in biomedical research. Cancer driver genes drive the development and progression of cancer, thus identifying cancer driver genes and their regulatory mechanism is crucial to the design of cancer treatment and intervention. Many computational methods, which take the advantages of computer science and data science, have been developed to utilise multiple types of genomic data to reveal cancer drivers and their regulatory mechanism behind cancer development and progression. Due to the complexity of the mechanistic insight of cancer genes in driving cancer and the fast development of the field, it is necessary to have a comprehensive review about the current computational methods for discovering different types of cancer drivers. Results: We survey computational methods for identifying cancer drivers from genomic data. We categorise the methods into three groups, methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. We also conduct a case study to compare the performance of the current methods. We further analyse the advantages and limitations of the current methods, and discuss the challenges and future directions of the topic. In addition, we investigate the resources for discovering and validating cancer drivers in order to provide a one-stop reference of the tools to facilitate cancer driver discovery. The ultimate goal of the paper is to help those interested in the topic to establish a solid background to carry out further research in the field., Comment: 13 pages, 6 figures

A central question in many fields of scientific research is to determine how an outcome would be affected by an action, or to measure the effect of an action (a.k.a treatment effect). In recent years, a need for estimating the heterogeneous treatment effects conditioning on the different characteristics of individuals has emerged from research fields such as personalized healthcare, social science, and online marketing. To meet the need, researchers and practitioners from different communities have developed algorithms by taking the treatment effect heterogeneity modeling approach and the uplift modeling approach, respectively. In this paper, we provide a unified survey of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We then provide a structured survey of existing methods by emphasizing on their inherent connections with a set of unified notations to make comparisons of the different methods easy. We then review the main applications of the surveyed methods in personalized marketing, personalized medicine, and social studies. Finally, we summarize the existing software packages and present discussions based on the use of methods on synthetic, semi-synthetic and real world data sets and provide some general guidelines for choosing methods., Comment: To appear in ACM Computing Surveys

This paper discusses the problem of causal query in observational data with hidden variables, with the aim of seeking the change of an outcome when "manipulating" a variable while given a set of plausible confounding variables which affect the manipulated variable and the outcome. Such an "experiment on data" to estimate the causal effect of the manipulated variable is useful for validating an experiment design using historical data or for exploring confounders when studying a new relationship. However, existing data-driven methods for causal effect estimation face some major challenges, including poor scalability with high dimensional data, low estimation accuracy due to heuristics used by the global causal structure learning algorithms, and the assumption of causal sufficiency when hidden variables are inevitable in data. In this paper, we develop a theorem for using local search to find a superset of the adjustment (or confounding) variables for causal effect estimation from observational data under a realistic pretreatment assumption. The theorem ensures that the unbiased estimate of causal effect is included in the set of causal effects estimated by the superset of adjustment variables. Based on the developed theorem, we propose a data-driven algorithm for causal query. Experiments show that the proposed algorithm is faster and produces better causal effect estimation than an existing data-driven causal effect estimation method with hidden variables. The causal effects estimated by the proposed algorithm are as accurate as those by the state-of-the-art methods using domain knowledge., Comment: 8 pages and 7 figures. The paper has been accepted by ECAI2020. We have updated the proof of the Theorem 1 and removed Theorem 2 from the conference version

In personalised decision making, evidence is required to determine whether an action (treatment) is suitable for an individual. Such evidence can be obtained by modelling treatment effect heterogeneity in subgroups. The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual. In this paper, we design a \emph{Treatment effect pattern (TEP)} to represent treatment effect heterogeneity in data. To achieve an interpretable presentation of TEPs, we use a local causal structure around the outcome to explicitly show how those important variables are used in modelling. We also derive a formula for unbiasedly estimating the \emph{Conditional Average Causal Effect (CATE)} using the local structure in our problem setting. In the discovery process, we aim at minimising heterogeneity within each subgroup represented by a pattern. We propose a bottom-up search algorithm to discover the most specific patterns fitting individual circumstances the best for personalised decision making. Experiments show that the proposed method models treatment effect heterogeneity better than three other existing tree based methods in synthetic and real world data sets., 17

Multi-instance learning (MIL) deals with tasks where data is represented by a set of bags and each bag is described by a set of instances. Unlike standard supervised learning, only the bag labels are observed whereas the label for each instance is not available to the learner. Previous MIL studies typically follow the i.i.d. assumption, that the training and test samples are independently drawn from the same distribution. However, such assumption is often violated in real-world applications. Efforts have been made towards addressing distribution changes by importance weighting the training data with the density ratio between the training and test samples. Unfortunately, models often need to be trained without seeing the test distributions. In this paper we propose possibly the first framework for addressing distribution change in MIL without requiring access to the unlabeled test data. Our framework builds upon identifying a novel connection between MIL and the potential outcome framework in causal effect estimation. Experimental results on synthetic distribution change datasets, real-world datasets with synthetic distribution biases and real distributional biased image classification datasets validate the effectiveness of our approach., In Proceedings of the Twenty-Fourth European Conference on Artificial Intelligence (ECAI'20)

Made available in DSpace on 2020-12-11T01:57:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-10-29 Griffith University Gowonda HPC Cluster Queensland Cyber Infrastructure Foundation Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research. Griffith Univ, Sch Informat & Commun Technol, Gold Coast, Qld 4222, Australia Griffith Univ, Inst Glyc, Gold Coast, Qld 4222, Australia Univ Colorado, Dept Med Med Oncol, Anschutz Med Campus, Denver, CO USA Tanta Univ, Fac Sci, Bot Dept, Tanta, Egypt Friedrich Schiller Univ, Jena Univ Hosp, Inst Human Genet, Jena, Germany Univ Med Ctr Schleswig Holstein, Dept Radiat Oncol, Campus Kiel, Kiel, Germany ARS, USDA, Plum Isl Anim Dis Ctr, Greenport, NY 11944 USA Univ Jyvaskyla, Dept Biol & Environm Sci, Jyvaskyla, Finland Graz Univ Technol, Inst Environm Biotechnol, Graz, Austria CSIC, CNB, Natl Biotechnol Ctr, Dept Macromol Struct, Madrid, Spain Univ Hong Kong, Fac Dent, Oral & Maxillofacial Radiol Appl Oral Sci & Commu, Hong Kong, Peoples R China Univ Kansas, Div Entomol, Biodivers Inst, Lawrence, KS 66045 USA SUNY Albany, RNA Inst, Albany, NY 12222 USA Robert Koch Inst, Dept Methods Dev & Res Infrastruct, Berlin, Germany Martin Luther Univ Halle Wittenberg, Dept Surg & Conservat Pediat & Adolescent Med, Halle, Germany Indiana Univ Purdue Univ, IU Sch Informat & Comp, Dept BioHlth Informat, Indianapolis, IN 46202 USA Univ Reading, Sch Pharm Stem Cell Biol & Regenerat Med, Reading, Berks, England Dali Univ, Sch Engn, Dali City, Yunnan, Peoples R China Univ Hosp Munich LMU, Dept Psychiat & Psychotherapy, Munich, Germany Univ Tsukuba, Fac Life & Environm Sci, Ibaraki, Japan UniLaSalle, Aghyle, Beauvais, France Henry Ford Hlth Syst, Dept Immunol, Detroit, MI USA Zhejiang Univ, Sch Med, Sir Run Run Shaw Hosp, Dept Emergency, Hangzhou 310016, Zhejiang, Peoples R China Ist Nazl Tumori Fdn Pascale IRCCS, Anesthesia & Pain Med, Naples, Italy NIH, Dept Transfus Med, Bethesda, MD USA Univ Glasgow, Inst Infect Immun & Inflammat, Glasgow, Lanark, Scotland Polish Acad Sci, Inst Human Genet, Poznan, Poland Univ Padua, Dept Surg Oncol & Gastroenterol DISCOG, Padua, Italy Eindhoven Univ Technol, Biomed Engn, Eindhoven, Netherlands Univ Strasbourg, IBMC, Strasbourg, France Heidelberg Univ, Dept Gen Visceral & Transplantat Surg, Heidelberg, Germany Univ Notre Dame, Psychol, Notre Dame, IN 46556 USA Harvard Med Sch, Massachusetts Gen Hosp, Radiol & Pathol, Boston, MA 02115 USA Auburn Univ, Dept Biol Sci, Auburn, AL 36849 USA KK Womens & Childrens Hosp, KK Res Ctr, Singapore, Singapore Univ Alabama, Educ Psychol, Tuscaloosa, AL USA UCL, Wellcome EPSRC Ctr Intervent & Surg Sci, London, England Maastricht Univ, Dept Nutr & Movement Sci, Maastricht, Netherlands Univ Arkansas Med Sci, Dept Radiat Oncol, Little Rock, AR 72205 USA Univ Padua, Dept Land Environm Agr & Forestry, Padua, Italy Univ Gothenburg, Dept Biol & Environm Sci, Gothenburg, Sweden Univ Newcastle, Prior Res Ctr Reprod Sci, Callaghan, NSW, Australia Univ Cyprus, Med Sch, Nicosia, Cyprus Shandong Agr Univ, Coll Plant Protect, Agr Big Data Res Ctr, Tai An, Shandong, Peoples R China NIMH, Neuropsychol Lab, Bldg 9, Bethesda, MD 20892 USA Univ Wisconsin, Wisconsin Inst Discovery, Madison, WI USA Univ Oxford, Struct Genom Consortium, Oxford, England Weihenstephan Triesdorf Univ Appl Sci, TUM Campus Straubing Biotechnol & Sustainabil, Bioinformat, Straubing, Germany Univ Michigan, Cardiovasc Res, Ann Arbor, MI 48109 USA Bombay Coll Pharm, Pharmaceut Chem, Mumbai, Maharashtra, India Inst Invest Biol Clemente Estable, Dev Neurobiol, Montevideo, Uruguay Gem Hosp & Res Ctr, Surg Gastroenterol & HPB Surg, Coimbatore, Tamil Nadu, India Kafr El Sheikh Univ, Fac Sci, Dept Zool, Kafr Al Sheikh, Egypt Med Univ Innsbruck, Dept Internal Med 4, Innsbruck, Austria Australian Natl Univ, Div Biomed Sci & Biochem, Canberra, ACT, Australia Univ Cattolica Sacro Cuore, Appl Technol Neuropsychol Lab, Milan, Italy Shandong Inst Parasit Dis, Med Entomol, Jinan, Shandong, Peoples R China Kumamoto Univ, Sch Pharm, Kumamoto, Japan Nagoya City Univ, Grad Sch Pharmaceut Sci, Dept Biol Chem, Nagoya, Aichi, Japan Univ Karachi, ICCBS, PCMD, Jamil ur Rahman Ctr Genome Res, Karachi 75270, Pakistan Labormed Zentrum Dr Risch, Vaduz, Liechtenstein Univ Otago, Med, Dunedin, New Zealand KK Womens & Childrens Hosp, Maternal Fetal Med, Singapore, Singapore Albert Ludwigs Univ Freiburg, GERN Tissue Replacement Regenerat & Neogenesis, Dept Orthoped & Trauma Surg, Med Ctr,Fac Med, Freiburg, Germany Heinrich Heine Univ, Inst Biochem & Mol Biol, Dusseldorf, Germany Natl Univ Ireland Galway, Surg, Galway, Ireland Univ Western Australia, Inst Agr, Perth, WA, Australia Univ Oxford, Nuffield Dept Med, Oxford, England SingHlth Polyclin, Punggol Polyclin, Singapore, Singapore Med Univ Innsbruck, Bioctr, Div Biol Chem, Innsbruck, Austria La Trobe Univ, La Trobe Inst Mol Sci, Biochem & Genet, Melbourne, Vic, Australia Univ Bordeaux, INRA, Bordeaux, France Agr & Agri Food Canada, Lethbridge Res & Dev Ctr, Lethbridge, AB, Canada St George Hosp, Trauma & Orthopaed, London, England Max Planck Inst Dev Biol, Dept Prot Evolut, Tubingen, Germany Med Univ Lublin, Dept Gynaecol 2, Lublin, Poland ICIT, ICSI Analyt Natl Res & Dev, Ramnicu Valcea, VL, Romania Univ KwaZulu Natal, Occupat Therapy, Westville Campus, Durban, South Africa Inst Canc Res, Joint Dept Phys, London, England Karolinska Inst, Dept Mol Med & Surg, Solna, Sweden Wenzhou Med Univ, Affiliated Hosp 2, Orthopaed, Wenzhou, Zhejiang, Peoples R China Univ Republica, Fac Ciencias, Ctr Invest Nucl, Lab Virol Mol, Montevideo, Uruguay Univ Gothenburg, Dept Med Biochem & Cell Biol, Gothenburg, Sweden Charite Med Univ Berlin, Pediat Cardiol, Berlin, Germany Bambino Gesu Pediat Hosp, Dept Neurosci & Neurorehabil, Neurorehabil Unit, Rome, Italy Indiana Univ Sch Med Northwest, Microbiol & Immunol, Gary, IN USA Univ Regensburg, Dept Behav & Mol Neurobiol, Regensburg, Germany Univ Queensland, Diamantina Inst, Brisbane, Qld, Australia Translat Res Inst, Brisbane, Qld, Australia Univ Hong Kong, Sch Biomed Sci, Hong Kong, Peoples R China St Jude Childrens Res Hosp, Pharmaceut Sci Dept, 332 N Lauderdale St, Memphis, TN 38105 USA Univ Durham, Mus, Durham, England Univ Ioannina, Med Sch, Dept Hyg & Epidemiol, Ioannina, Greece Tech Univ Munich, Sch Life Sci Weihenstephan, Maximus von Imhof Forum 3, D-85354 Freising Weihenstephan, Germany Univ Hosp Essen, Inst Transfus Med, Essen, Germany Virginia Commonwealth Univ, Comp Sci, Richmond, VA USA Univ Queensland, Inst Social Sci Res, Brisbane, Qld, Australia Univ Lyon, Ctr Int Rech Infectiol, Lyon, France Griffith Univ, Sch Allied Hlth Sci, Gold Coast, Qld, Australia Univ Melbourne, Dept Biochem & Mol Biol, Parkville, Vic, Australia Indiana Univ Purdue Univ, Dept Biohlth Informat, Sch Informat & Comp, Indianapolis, IN 46202 USA Philipps Univ Marburg, Inst Lung Res, Marburg, Germany Indiana Univ, Dept Biol, Bloomington, IN USA Univ Florida, Oral Biol, Gainesville, FL USA Univ Colorado, Dept Biochem, Boulder, CO 80309 USA Kyoto Univ, Ctr Promot Interdisciplinary Educ & Res, Kyoto, Japan Friedrich Loeffler Inst, Inst Virus Diagnost, Greifswald, Germany Univ Oxford, Dept Zool, Oxford, England Tech Univ Munich, Dept Bioinformat, Munich, Germany Univ Adelaide, Sch Anim & Vet Sci, Adelaide, SA, Australia Stanford Univ, Canc Biol, Palo Alto, CA 94304 USA Covenant Univ, Dept Elect & Informat Engn, Ota, Nigeria Heinrich Heine Univ, Inst Stem Cell Res & Regenerat Med, Dusseldorf, Germany Univ British Columbia, Fac Pharmaceut Sci, Vancouver, BC, Canada Albert Einstein Coll Med, Psychiat, New York, NY USA Akdeniz Univ, Med Biol & Genet, Antalya, Turkey NIMH, NIH, Bethesda, MD 20892 USA Med Univ Hosp, Internal Med 1, Tubingen, Germany NET ESolut, Netelabs, Mclean, VA USA Univ Southern Denmark, Inst Publ Hlth, Odense, Denmark John Innes Ctr, Mol Microbiol, Norwich, Norfolk, England Univ Adelaide, Waite Res Precinct, Australian Res Council, Ctr Excellence Plant Energy Biol, Adelaide, SA, Australia Univ Cambridge, Cambridge Inst Publ Hlth, Cambridge, England Univ Munster, Inst Biochem, Munster, Germany Australian Natl Univ, Natl Ctr Epidemiol & Populat Hlth RSPH, Canberra, ACT, Australia Stanford Univ, Neurobiol & Bioengn, Palo Alto, CA 94304 USA Univ Birmingham, Geog Earth & Environm Sci, Birmingham, W Midlands, England Murdoch Univ, Sch Vet & Life Sci, Perth, WA, Australia George Mason Univ, Ctr Climate Change Commun, Fairfax, VA 22030 USA Univ Adelaide, Sch Agr Food & Wine, Adelaide, SA, Australia Southern Med Univ, Sch Pharmaceut Sci, Guangzhou, Guangdong, Peoples R China Stanford Univ, Pathol, Palo Alto, CA 94304 USA Univ Bologna, Dept Expt Diagnost & Specialty Med, Bologna, Italy Univ Padua, Dept Gen Psychol, Padua, Italy Huazhong Univ Sci & Technol, Coll Life Sci & Technol, Dept Bioinformat & Syst Biol, Key Lab Mol Biophys,Minist Educ, Wuhan, Hubei, Peoples R China Huazhong Univ Sci & Technol, Collaborat Innovat Ctr Biomed Engn, Wuhan, Hubei, Peoples R China Queensland Univ Technol, Sch Biomed Sci, Brisbane, Qld, Australia French Natl Ctr Sci Res, CNRS, Inst Biol Physicochim, Paris, France Univ Warwick, Dept Sociol, Coventry, W Midlands, England European Mol Biol Lab, Struct & Computat Biol, Heidelberg, Germany Univ Cincinnati, Coll Med, Canc Biol, Cincinnati, OH USA Univ Waterloo, Biol, Waterloo, ON, Canada Sorbonne Univ, CNRS, Integrat Biol Marine Models LBI2M, SBR, Roscoff, France Univ Manitoba, Biochem & Med Genet, Winnipeg, MB, Canada Max Planck Inst Solid State Res, Ultrafast Solid State Spect, Stuttgart, Germany Imperial Coll London, Sch Publ Hlth, Dept Infect Dis Epidemiol, London, England Mansoura Univ, Fac Vet Med, Biochem, Mansoura, Egypt Univ Virginia, Mol Physiol & Biol Phys, Charlottesville, VA USA China Univ Geosci, State Key Lab Biogeol & Environm Geol, Wuhan, Hubei, Peoples R China Clin Neurol & Psychiat Children & Youth, Child Psychiat, Belgrade, Serbia Ahvaz Jundishapur Univ Med Sci, Med Phys, Ahwaz, Iran Univ Iowa City, Coll Dent, Div Biostat & Computat Biol, Dept Prevent & Community Dent, Iowa City, IA USA Univ Iowa City, Coll Dent, Div Biostat & Computat Biol, Dept Biomed Engn, Iowa City, IA USA Univ Iowa City, Coll Dent, Div Biostat & Computat Biol, Dept Biostat, Iowa City, IA USA Univ Tubingen, Inst Med Psychol & Behav Neurobiol, Tubingen, Germany Univ Ibadan, Dept Zool, Ibadan, Nigeria Kansas State Univ, Coll Vet Med, Manhattan, KS 66506 USA Univ Tasmania, Sch Technol Environm & Design, Discipline ICT, Hobart, Tas, Australia Childrens Mercy Hosp, Radiol, Kansas City, MO 64108 USA Stockholm Univ, Dept Phys, Stockholm, Sweden Oslo Univ Hosp, Inst Canc Genet & Informat, Oslo, Norway CSIRO, CSIRO Mfg, Pullenvale, Qld, Australia Johns Hopkins Sch Med, Urol, Baltimore, MD USA Univ South Africa, Life & Consumer Sci, Johannesburg, South Africa German Ctr Neurodegenerat Dis, Mech Induced Plast Brain, Bonn, Germany UCL, Inst Child Hlth, Dev Biol & Canc, London, England Simon Fraser Univ, Biomed Physiol & Kinesiol, Burnaby, BC, Canada Univ Manchester, Ctr Hlth Informat, Manchester, Lancs, England Univ Queensland, Sch Human Movement & Nutr Sci, Brisbane, Qld, Australia Albert Einstein Coll Med, Dept Med, New York, NY USA Georgetown Univ, Kennedy Inst Eth, Washington, DC 20057 USA Dermatol Skin & Canc Fdn, Carlton, Vic, Australia Med Coll Wisconsin, Div Hematol, Milwaukee, WI 53226 USA Med Coll Wisconsin, Div Oncol, Milwaukee, WI 53226 USA Med Coll Wisconsin, Div Infect Dis, Milwaukee, WI 53226 USA US FDA, Off Biotechnol Prod, Washington, DC 20204 USA Worcester Polytech Inst, Biomed Engn, Worcester, MA 01609 USA Univ Adelaide, Ctr Orthopaed & Trauma Res, Adelaide, SA, Australia Publ Hlth England, Natl Infect Serv, Bristol, Avon, England Univ Utrecht, Fac Sci Chem, Utrecht, Netherlands Natl Univ Ireland Galway, Pathol, Galway, Ireland Imperial Coll London, NHLI, London, England Danube Private Univ, Neurodegenerat, Krems Donau, Austria Imperial Coll London, Dept Chem, London, England Univ Helsinki, Finnish Museum Nat Hist, Helsinki, Finland Univ Sydney, Sch Life & Environm Sci, Sydney, NSW, Australia Univ Nottingham, Nottingham Digest Dis Ctr, Nottingham, England Univ Toulouse, CNRS, ITAV, USR3505, Toulouse, France ASCR, Inst Mol Genet, CZ Openscreen, Prague, Czech Republic Univ Turku, Inst Biomed, Turku, Finland York Univ, Sch Kinesiol & Hlth Sci, Toronto, ON, Canada Univ Calif Los Angeles, Stein Eye Inst, Ophthalmol, Los Angeles, CA USA Agr & Agri Food Canada, Ottawa Res & Dev Ctr, Ottawa, ON, Canada Helmholtz Zentrum Geesthacht, Mat Design & Characterizat, Geesthacht, Germany Unvers Genova, Internal Med, Genoa, Italy Otto von Guericke Univ, Intelligent Catheter INKS, Magdeburg, Germany Univ Toledo, Canc Biol, 2801 W Bancroft St,Hlth Sci Campus, Toledo, OH 43606 USA CRUK Beatson Inst, Struct Biol, Glasgow, Lanark, Scotland Leibniz Inst Primate Res, Med RNA Biol, Gottingen, Germany Univ Limoges, PEIRENE, EA 7500, Limoges, France Hainan Univ, Vet Med, Haikou, Hainan, Peoples R China UCL, Sch Pharam, Pharmacognosy & Phytotherapy, London, England Univ Oulu, Ecol & Genet Res Unit, Oulu, Finland Rhein Westfal TH Aachen, Inst Biochem & Mol Immunol, Aachen, Germany Univ Stuttgart, Inst Biochem & Tech Biochem, Stuttgart, Germany Peking Univ, Shenzhen Grad Sch, Sch Chem Biol & Biotechnol, Shenzhen, Peoples R China GIGA Res Inst, Med Oncol, Liege, Belgium CHULiege, Liege, Belgium Fdn Bruno Kessler, MPBA, Trento, Italy Hokkaido Univ, Grad Sch Med, Dept Neurobiol, Sapporo, Hokkaido, Japan Univ Leuven, Oncol, Leuven, Belgium Chalmers Univ Technol, Dept Math Sci, Gothenburg, Sweden Radboud Univ Nijmegen, Med Ctr, Dept Neurol, Nijmegen, Netherlands Univ South Australia, Sch Informat Technol & Math Sci, Adelaide, SA, Australia Bio Thera Solut Ltd, Dept Computat Biol, Guangzhou, Guangdong, Peoples R China Inst Invest Biosanitario Granada IBS, Otolaryngol, Granada, Spain Curtin Univ, Sch Mol & Life Sci, Perth, WA, Australia Newcastle Univ, Inst Cellular Med, Newcastle Upon Tyne, Tyne & Wear, England Goethe Univ, Biol DCAL, Inst Pharm, Frankfurt, Germany Univ Lyon, ICBMS, UMR 5246, Lyon, France Inst Pasteur, Dept Genomes & Genet, Paris, France Univ Valencia, Pharmacol, Valencia, Spain Council Agr Res & Econ, Res Ctr Olive Citrus & Tree Fruit, Caserta, Italy Fraunhofer Inst Toxicol & Expt Med ITEM, Preclin Pharmacol & Vitro Toxicol, Hannover, Germany Univ Tasmania, Sch Med, Hobart, Tas, Australia Chugai Pharmaceut Co Ltd, Oncol Lifecycle Management Dept, Tokyo, Japan Univ Lubeck, Inst Neurobiol, Lubeck, Germany NYU, Sch Med, Neurol, New York, NY USA Univ Putra Malaysia, Dept Plant Pathol, Seri Kembangan, Malaysia Univ N Carolina, Biol, Chapel Hill, NC 27515 USA Univ Southampton, Fac Hlth Sci, Southampton, Hants, England Univ Highlands & Islands, Genet & Immunol Res Grp, Inverness, Scotland Heidelberg Univ, Inst Pathol, Heidelberg, Germany Indraprastha Inst Informat Technol, Dept Computat Biol, New Delhi, India Glasgow Caledonian Univ, Sch Hlth & Life Sci, Glasgow, Lanark, Scotland Liverpool John Moores Univ, Pharm & Biomol Sci, Liverpool, Merseyside, England Parkinsons Inst & Clin Ctr, Basic Res, Sunnyvale, CA USA Luxembourg Inst Sci & Technol, Environm Res & 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Kansas, Med Ctr, Family Med Res Div, Kansas City, KS 66103 USA ASTAR, Inst Mol & Cell Biol, Multimodal Mol Biol, Singapore, Singapore Univ Leuven, Dept Chron Dis Metab & Ageing, Leuven, Belgium Int Iberian Nanotechnol Lab INL, Braga, Portugal Anglia Ruskin, Comp & Technol, Cambridge, England Max Planck Inst Biochem, Struct Cell Biol, Planegg, Germany Univ Seville, Dept Comp Architecture & Technol, Seville, Spain Univ Colorado, EBIO, Boulder, CO 80309 USA Univ Manchester, Canc Sci, Manchester, Lancs, England Australian Natl Univ, Res Sch Populat Hlth, Canberra, ACT, Australia Singapore MIT Alliance Res & Technol, Biosyst, Singapore, Singapore Australian Catholic Univ, Mary MacKillop Inst Hlth Res, Musculoskeletal Hlth & Ageing Res Program, Melbourne, Vic, Australia Ruhr Univ Bochum, Gen Surg, St Josef Hosp, Bochum, Germany Northwest A&F Univ, Coll Life Sci, Xianyang, Shaanxi, Peoples R China Australian Natl Univ, Res Sch Biol, Div Plant Sci, Canberra, ACT, Australia Battelle Mem Inst, Clin & Nonclin Res, Columbus, OH USA Southern Methodist Univ, Biol Sci, Dallas, TX 75275 USA Teikyo Univ, Inst Med Mycol, Tokyo, Japan Tempus Labs, Bioinformat, Chicago, IL USA Hunan Univ, Coll Biol, Changsha, Hunan, Peoples R China Inst Cochin, Dept Infect Immun & Inflammat, Paris, France Royal Coll Surgeons Ireland, FutureNeuro Res Ctr Physiol & Med Phys, Dublin, Ireland Univ Jyvaskyla, Gerontol Res Ctr, Jyvaskyla, Finland Heidelberg Univ, Dept Anesthesiol, Heidelberg, Germany Penn State Univ, Biol, University Pk, PA 16802 USA Chinese Acad Sci, Inst Microbiol, State Key Lab Microbial Resources, Beijing, Peoples R China Univ Sci & Technol China, Sch Life Sci, Hefei, Anhui, Peoples R China Michigan State Univ, Anim Sci, E Lansing, MI 48824 USA SUNY Buffalo, Jacobs Sch Med & Biomed Sci, Dept Neurol, Buffalo Neuroimaging Anal Ctr, New York, NY USA Univ Rostock, Inst Biol Sci, Rostock, Germany South China Normal Univ, Sch Environm, Environm Res Inst, Guangzhou, Guangdong, Peoples R China Univ Bari, Dept Vet Med, Bari, Italy Radboud Univ Nijmegen, Med Ctr, Primary & Community Care, Nijmegen, Netherlands EcoHlth Alliance, New York, NY USA Mayo Clin, Cardiovasc Dept, Rochester, MN USA Med Univ Silesia, Sch Med Katowice, Dept Epidemiol, Katowice, Poland Univ Lleida, Anim Sci, Lleida, Spain Univ Florida, Coll Pharm, Pharmaceut Outcomes & Policy, Gainesville, FL USA Pacific Northwest Natl Lab, Earth & Biol Sci Directorate, Richland, WA 99352 USA Univ Edinburgh, Ctr Discovery Brain Sci, Edinburgh, Midlothian, Scotland Harvard Med Sch, Dept Biomed Informat, Boston, MA 02115 USA Univ Penn, Radiol, Philadelphia, PA 19104 USA Humanitas Univ & Res Hosp, Asthma & Allergy Unit, Biomed Sci Personalized Med, Rozzano, Italy Australian Natl Univ, Dept Quantum Sci, Canberra, ACT, Australia Carl von Ossietzky Univ Oldenburg, Ecol Genom, Oldenburg, Germany Int Med Univ, Paediat Dent & Orthodont, Kuala Lumpur, Malaysia Univ Ottawa, Sch Nursing, Ottawa, ON, Canada Maastricht Univ, Dept Educ Support, Maastricht, Netherlands Univ Manchester, Math, Manchester, Lancs, England Kings Coll London, Fac Life Sci & Med, Sch Populat Hlth Sci, London, England Queensland Univ Technol, Inst Hlth & Biomed Innovat, Brisbane, Qld, Australia Harbin Med Univ, Publ Hlth Sch, Epidemiol, Harbin, Heilongjiang, Peoples R China UiT Arctic Univ Norway, Dept Chem, Tromso, Norway Cornell Univ, Biol Stat & Computat Biol, Ithaca, NY USA East China Normal Univ, Sch Ecol & Environm Sci, Shanghai Key Lab Urban Ecol Proc & Ecorestorat, Shanghai, Peoples R China Johannes Gutenberg Univ Mainz, Fac Biol, Mainz, Germany Univ Massachusetts, Food Sci, Amherst, MA 01003 USA Max Planck Inst Terr Microbiol, Complex Adapt Traits Res Grp, Marburg, Germany NIHR Biomed Res Ctr Resp, Ctr Exercise & Rehabil Sci, Leicester, Leics, England Yale Univ, Dept Internal Med, Sect Endocrinol, New Haven, CT USA Sardar Patel Univ, Dept Biosci, Anand, Gujarat, India Univ Tokyo, Dept Appl Phys, Tokyo, Japan Univ Cologne, Vivo Res Facil ivRF, Cologne Excellence Cluster Cellular Stress Respon, Cologne, Germany Natl Open Univ Nigeria, Dept Publ Hlth Sci, Lagos, Nigeria Univ Hosp Regensburg, Dept Radiol, Regensburg, Germany Natl Univ Singapore, Geog, Singapore, Singapore Alfaisal Univ, Coll Med, Riyadh, Saudi Arabia Abdelmalek Essaadi Univ, Fac Sci & Techn Tangier, Biomed Genom & Oncogenet Res Lab, Tetouan, Morocco Tech Univ Munich, Fac Sport & Hlth Sci, Exercise Biol Grp, Munich, Germany Univ Santiago de Compostela, Dept Psicoloxia Social Basica & Metodoloxia, Galiza, Spain Griffith Univ, Signal Proc Lab, Brisbane, Qld, Australia Univ Ghent, Dept Neurol, Ghent, Belgium Ghent Univ Hosp, Ghent, Belgium Univ Ghent, Fac Vet Med, Merelbeke, Belgium Albert Einstein Coll Med, Inst Clin & Translat Res, New York, NY USA Anglia Ruskin Univ, Fac Med Sci, Cambridge, England Peking Univ, Coll Chem & Mol Engn, Beijing, Peoples R China Herlev & Gentofte Hosp, Dept Dermatol & Allergy, Hellerup, Denmark Lady Cilento Childrens Hosp, Med Imaging & Nucl Med, Brisbane, Qld, Australia Univ Gambia, Sch Med & Allied Hlth Sci, Nursing & Reprod Hlth, Brikama, Gambia Univ Sydney, Woolcock Inst Med Res, Sydney, NSW, Australia Wayne State Univ, Comp Sci, Detroit, MI USA Aberdeen Royal Infirm, Otolaryngol, Aberdeen, Scotland Univ Toronto, Lab Med & Pathobiol, Toronto, ON, Canada Chinese Univ Hong Kong, Inst Ageing, Hong Kong, Peoples R China Univ Nebraska Med Ctr, Emergency Med, Omaha, NE USA Univ Florida, Coll Med, Pathol, Gainesville, FL USA Univ Texas MD Anderson Canc Ctr, Radiat Oncol, Houston, TX 77030 USA Univ Pisa, Translat Res NTMS, Pisa, Italy Magna Grecia Univ, Clin & Expt Med, Catanzaro, Italy Univ Vermont, Larner Coll Med, Pediat, Burlington, VT USA Sun Yat Sen Univ, Canc Ctr, Diagnost & Intervent Ultrasound, Guangzhou, Guangdong, Peoples R China Fudan Univ, Inst Brain Sci, Shanghai, Peoples R China Shanxi Agr Univ, Coll Agron, Jinzhong, Shanxi, Peoples R China Univ Toronto, Inst Med Sci, Toronto, ON, Canada Univ Adelaide, ARCPOH, Adelaide, SA, Australia Pelita Harapan Univ, Fac Med, Cardiol & Vasc Med, Tangerang, Indonesia Inst Mental Hlth, Res Div, Singapore, Singapore UCL, MRC Clin Trials Unit, London, England Univ Padua, Dept Philosophy Sociol Educ & Appl Psychol FISPPA, Padua, Italy Univ Lubeck, Dept Psychiat & Psychotherapy, Lubeck, Germany Dalian Univ Technol, Ctr Mol Med, Dalian, Liaoning, Peoples R China Tianjin United Family Healthcare, Reprod Med, Tianjin, Peoples R China Univ Autonoma Barcelona, Dept Engn Quim Biol & Ambiental, Barcelona, Spain Sao Paulo State Univ UNESP, Dept Anim Sci, Sao Paulo, Brazil Helmholtz Zentrum Munchen, Inst Computat Biol, Canc Syst Biol, Ingolstadter Land Str 1, D-85764 Munich, Germany Univ Tokyo, Dept Cardiovasc Med, Tokyo, Japan Sao Paulo State Univ, Vet Clin, Sao Paulo, Brazil Zhejiang Univ, Inst Biotechnol, Hangzhou, Zhejiang, Peoples R China Aarhus Univ, Dept Mol Biol & Genet, Aarhus, Denmark Univ Manchester, St Marys Hosp, Maternal & Fetal Hlth, Manchester, Lancs, England Univ New Mexico, Internal Med, Albuquerque, NM 87131 USA Mayo Clin, Div Biomed Stat & Informat, Jacksonville, FL 32224 USA King Saud Univ, Plant Prod, Riyadh, Saudi Arabia Polish Acad Sci, Inst Genet & Anim Breeding, Dept Mol Biol, Warsaw, Poland Queensland Univ Technol, Optometry & Vis Sci, Brisbane, Qld, Australia Univ Nottingham, Sch Life Sci, Nottingham, England Canc Council Queensland, Canc Res Ctr, Brisbane, Qld, Australia Umea Univ, Dept Chem, Umea, Sweden Publ Hlth England, Ctr Radiat Chem & Environm Hazards, Bristol, Avon, England Univ Campania L Vanvitelli, DISTABIF, Caserta, Italy Bartshealth, Obstet & Gyanecol, London, England Univ Clermont Auvergne, GReD Lab, Clermont Ferrand, France INRA Abeilles & Environm, Avignon, France Queensland Univ Technol, Sch Publ Hlth & Social Work, Brisbane, Qld, Australia Univ Penn, Psychiat, Philadelphia, PA 19104 USA Novosibirsk State Univ, Res Inst Physiol & Basic Med, Novosibirsk, Russia UCL, Dept Chem, London, England La Trobe Univ, Anim Plant & Soil Sci, Melbourne, Vic, Australia Univ Arizona, Epidemiol & Biostat, Tucson, AZ USA Univ Groningen, Univ Med Ctr Groningen, ERIBA, Groningen, Netherlands Univ Gothenburg, Inst Clin Sci, Dept Radiat Phys, Gothenburg, Sweden SUNY Upstate Med Univ, Biochem & Mol Biol, Syracuse, NY 13210 USA Fundacao Oswaldo Cruz, Ctr Pesquisas Goncalo Moniz, Salvador, Bahia, Brazil Inst Environm Sci & Res ESR, Food Water & Environm Microbiol, Christchurch, New Zealand Univ Otago, Food Sci, Dunedin, New Zealand Florida Atlantic Univ, Biomed Sci, Boca Raton, FL 33431 USA Univ Queensland, Inst Mol Biosci, Brisbane, Qld, Australia Univ Hosp Wurzburg, Inst Clin Neurobiol, Wurzburg, Germany Inst Trop Med, Publ Hlth, Antwerp, Belgium Univ Thessaly, Sch Hlth Sci, Fac Med, Dept Rheumatol & Clin Immunol, Larisa, Greece Univ Basel, UZB Univ Ctr Dent Med, Dept Orthodont & Pediat Dent, Basel, Switzerland Sorbonne Univ, Museum Natl Hist Nat, Inst Systemat Evolut Biodiversite ISYEB, MNHN,CNRS,UMR 7205,EPHE, Paris, France CNRS, Inst Adv Biosci, Paris, France Univ Western Australia, Sch Mol Sci, Perth, WA, Australia Univ Ft Hare, Biochem & Microbiol, Alice, South Africa Univ Tubingen, Phys, Tubingen, Germany Griffith Univ, Sch Environm & Sci, Brisbane, Qld, Australia Philosoph Theol Hsch Vallendar, Stat & Standardised Methods, Vallendar, Germany Imperial Coll London, Life Sci, London, England Univ Adelaide, Adelaide Med Sch, Adelaide, SA, Australia Univ Nebraska Med Ctr, Dept Pharmacol & Expt Neurosci, Omaha, NE USA Technol Univ Dublin, FOCAS Res Inst, Dublin, Ireland INSERM, Natl Inst Hlth & Med Res, Canc Res Ctr Toulouse, Paris, France Univ Santiago de Compostela, Dept Bioloxia Func, Grp BRAINSHARK, Galiza, Spain Univ Nebraska, Biol, Kearney, NE USA Univ Autonoma Barcelona, Dept Genet & Microbiol, Barcelona, Spain Univ Pisa, Chem & Ind Chem, Pisa, Italy Univ Manchester, Wellcome Trust Ctr Cell Matrix Res, Manchester, Lancs, England Univ Montpellier, CNRS, Inst Human Genet, Montpellier, France Czech Acad Sci, Inst Organ Chem & Biochem, Prague, Czech Republic CSIC, Natl Ctr Biotechnol CNB, Computat Syst Biol Grp, Madrid, Spain Natl Cheng Kung Univ, Dept Biochem & Mol Biol, Tainan, Taiwan Harvard Med Sch, Schepens Eye Res Inst, Ophthalmol, Boston, MA 02115 USA Lanzhou Univ, Hosp 2, Dept Gen Surg, Lanzhou, Gansu, Peoples R China Univ Technol Sydney, Sch Life Sci, Sydney, NSW, Australia JT Chen Clin, Gynecol, Tokyo, Japan Univ Western Australia, Sch Agr & Environm, Perth, WA, Australia Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China Univ Missouri, Elect Engn & Comp Sci, Columbia, MO USA Vrije Univ Amsterdam Med Ctr, Amsterdam Publ Hlth Res Inst, Dept Publ & Occupat Hlth, Amsterdam, Netherlands Semmelweis Univ, Med Biochem, Budapest, Hungary Queen Elizabeth Hosp, Dept Clin Oncol, Hong Kong, Peoples R China Univ Pittsburgh, Chem, Pittsburgh, PA USA GB Pant Inst Post Grad Med Educ & Res, Neurol, New Delhi, India Univ Copenhagen, Vet & Anim Sci, Copenhagen, Denmark Univ Palermo, Biomed Dept Internal & Specialist Med DIBIMIS, Sect Endocrinol, Palermo, Italy UCL, NPP, London, England Univ Florida, Microbiol & Cell Sci, Gainesville, FL USA Univ Salford, Sch Hlth Sci, Manchester, Lancs, England Cardiff Univ, Inst Med Genet, Cardiff, S Glam, Wales INSERM, ICO Canc Ctr, Angers, France Univ Colombo, Fac Med, Microbiol, Colombo, Sri Lanka Beijing Univ Chinese Med, Sch Chinese Mat Med, Beijing, Peoples R China Univ Porto, Fac Pharm, Porto, Portugal Univ Nottingham, Div Primary Care, Nottingham, England Univ Illinois, Pharm Syst Outcomes & Policy, Chicago, IL USA Pontificia Univ Catolica Goias, Escola Ciencias Agr & Biol, Goiania, Go, Brazil China Japan Friendship Hosp, Dept Oncol, Beijing, Peoples R China Boston Univ, Chem, Boston, MA 02215 USA Amer Univ, Environm Sci, Washington, DC 20016 USA Carleton Univ, Neurosci, Ottawa, ON, Canada Univ Regina, Chem & Biochem, Regina, SK, Canada Univ Montreal, Microbiolgy, Montreal, PQ, Canada Univ Leicester, Dept Genet & Genome Biol, Leicester, Leics, England Univ Queensland, Sch Agr & Food Sci, Gatton, Qld, Australia CNRS, BIOM, Paris, France UCL, Hatter Cardiovasc Inst, London, England Flinders Univ S Australia, Sci & Engn, Adelaide, SA, Australia Univ Warwick, Engn, Coventry, W Midlands, England Katholieke Univ Leuven, Ctr Human Genet, Leuven, Belgium Fudan Univ, Dept Macromol Sci, Shanghai, Peoples R China Dokuz Eylul Univ, Biol Educ, Izmir, Turkey Indiana Univ Sch Med, Dept Biochem & Mol Biol, Indianapolis, IN 46202 USA Mondo Med, Pulm Dis, Borgomanero, Italy US Naval, Res Lab, Ctr Bio Mol Sci & Engn, Washington, DC USA Univ Oslo, Inst Basic Med Sci, Dept Nutr, Oslo, Norway Peking Univ, Dept Mech & Engn Sci, Beijing, Peoples R China Saarland Univ, Dept Pharm Pharmaceut & Med Chem, Saarbrucken, Germany INSERM, Natl Inst Hlth & Med Res, Skin Res Inst, Paris, France Shanghai Proton & Heavy Ion Ctr, Res & Dev, Shanghai, Peoples R China Univ Georgia, Epidemiol, Athens, GA 30602 USA Univ Freiburg, Med Ctr, Dept Plast & Hand Surg, Freiburg, Germany Sidra Med, Res, Doha, Qatar Rostock Univ, Med Ctr, Dept Oral Maxillofacial & Plast Surg, Rostock, Germany Harvard Med Sch, Brigham & Womens Hosp, Emergency Med, Boston, MA 02115 USA AgResearch, Forage Sci, Palmerston North, New Zealand Univ Calif Los Angeles, Med, Los Angeles, CA USA Arizona State Univ, Biodesign Inst, Virginia G Piper Ctr Personalized Diagnost, Tempe, AZ USA Sheffield Hallam Univ, Res Inst, Mat & Engn, Sheffield, S Yorkshire, England Aneurin Bevan Univ Healthboard, Resp Med, Newport, Shrops, England Univ Calif San Francisco, Neurol Surg, San Francisco, CA 94143 USA Univ Western Australia, UWA Dent Sch, Perth, WA, Australia Fordham Univ, Biol Sci, Bronx, NY 10458 USA Univ Helsinki, Inst Biotechnol, Helsinki, Finland Fujian Normal Univ, Coll Life Sci, Fuzhou, Fujian, Peoples R China Univ Fukui, Dept Frontier Fiber Technol & Sci, Fukui, Japan Univ Southampton, Fac Med, Clin & Expt Sci, Southampton, Hants, England Univ Urbino, Dept Biomol Sci, Urbino, Italy Osped San Luigi, Allergol Unit, Turin, Italy Muljibhai Patel Urol Hosp, Dept Urol, Nadiad, Gujarat, India Univ Granada, Stratig & Paleontol, Granada, Spain Massey Univ, Sch Vet Sci, Auckland, New Zealand CNR, High Performance Comp & Networking Inst, Naples, Italy Univ Childrens Hosp Zurich, Div Metab, Zurich, Switzerland Univ Childrens Hosp Zurich, Childrens Res Ctr, Zurich, Switzerland Univ Melbourne, Biochem & Mol Biol, Parkville, Vic, Australia Inst Pasteur, Leptospirosis Res & Expertise Unit, Noumea, New Caledonia Tsinghua Univ, Sch Life Sci, Beijing, Peoples R China Cheikh Anta Diop Univ UCAD, Sci Fac, Biol Anim Dept, Dakar, Senegal Providence Portland Med Ctr, Earle A Chiles Res Inst, Portland, OR USA Agr & Agri Food Canada, Lacombe Res & Dev Ctr, Lacombe, AB, Canada Alberta Innovates, Performance Management & Evaluat, Edmonton, AB, Canada Univ Malaga, CSIC, Inst Hortofruticultura Subtrop Mediterranea La Ma, IHSM,UMA, Malaga, Spain James Cook Univ, Coll Publ Hlth Med & Vet Sci, Cairns, Qld, Australia European Commiss, Joint Res Ctr, Ispra, Italy Univ Montpellier, Montpellier, France CSIRO, Floreat, WA, Australia Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Guangzhou, Guangdong, Peoples R China Vanderbilt Univ, Pharmacol, 221 Kirkland Hall, Nashville, TN 37235 USA RIKEN, KFU RIKEN Translat Genom Unit, Yokohama, Kanagawa, Japan Univ Tubingen, Neurobiol Vocal Commun, Tubingen, Germany Univ Colorado, UCCS Ctr Biofrontiers Inst, Colorado Springs, CO 80907 USA Fred Hutchinson Canc Ctr, Div Basic Sci, Seattle, WA USA Univ Geneva, Fac Med, Primary Care Unit, Geneva, Switzerland Ernst Moritz Arndt Univ Greifswald, Dept Mol Genet & Infect Biol, Greifswald, Germany East China Univ Sci & Technol, Dept Fine Chem, Shanghai, Peoples R China Ohio State Univ, Surg, Columbus, OH 43210 USA Oragenics, R&D, Tampa, FL USA Natl Environm Agcy, Environm Hlth Inst, Singapore, Singapore Friedrich Loeffler Inst, Inst Diagnost Virol, Greifswald, Germany Queen Elizabeth Hosp, Oncol, Woodville, SA, Australia USDA, Emerging Pests & Pathogens Res Unit, Ithaca, NY USA Univ Freiburg, Med Ctr, Dept Neurosurg, Epilepsy Ctr, Freiburg, Germany Univ Hong Kong, Fac Educ, Informat & Technol Studies, Hong Kong, Peoples R China Univ Tokyo, Grad Sch Agr & life Sci, Agr & Environm Biol, Tokyo, Japan Univ Pittsburgh, Dept Med, Pittsburgh Heart Lung & Blood Vasc Med Inst, Pittsburgh, PA USA Guys & St Thomas NHS Fdn Trust, Directorate Transplant Renal & Urol, London, England Univ Sarajevo, Clin Ctr, Clin Heart Blood Vessel & Rheumat Dis, Sarajevo, Bosnia & Herceg Univ Kent, Sch Math Stat & Actuarial Sci, Canterbury, Kent, England Tokyo Inst Technol, Dept Life Sci & Technol, Tokyo, Japan Univ Appl Sci Munich, Laser Ctr Dept Appl Sci & Mechatron, Munich, Germany CIC NanoGUNE, Nanodevices, San Sebastian, Spain Vrije Univ Amsterdam Med Ctr, Gynaecol, Amsterdam, Netherlands Cardiff Univ, Med Sch, Div Populat Med, Cardiff, S Glam, Wales Karolinska Inst, Dept Med, Solna, Sweden Natl Inst Genet, Ctr Informat Biol, Mishima, Shizuoka, Japan Murdoch Univ, Harry Perkins Inst Med Res, Perth, WA, Australia Univ Limerick, Phys Educ & Sport Sci, Limerick, Ireland Ruhr Univ Bochum, Campus Clin Gynecol, Univ Str, Bochum, Germany Southwest Med Univ, Sch Publ Hlth, Epidemiol & Biostat, Luzhou, Sichuan, Peoples R China Beijing Canc Hosp, Minist Educ Beijing, Key Lab Carcinogenesis & Translat Res, Ctr Mol Diagnost, Beijing, Peoples R China Chinese Acad Sci, Chengdu Inst Biol, Herpetol Dept, Chengdu, Sichuan, Peoples R China Key Lab Nano Biol Effects & Safety, Beijing, Peoples R China NIBR, PK Sci, Basel, Switzerland Daejeon St Marys Hosp, Pain Ctr, Daejeon, South Korea Univ Western Ontario, Sch Hlth Studies, London, ON, Canada Univ Aberdeen, Hlth Psychol Grp, Aberdeen, Scotland Univ Colorado, Anesthesiol, Anschutz Med Campus, Boulder, CO 80309 USA Univ Antwerp, UZA Antwerp Univ Hosp, Crit 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Res Ctr, Stem Cell & Regenerat Med, Riyadh, Saudi Arabia Univ Bahri, Ind Pulp & Paper, Khartoum, Sudan Univ Queensland, Mater Med Res Inst, Mater Res Inst, Brisbane, Qld, Australia Colorado State Univ, NREL, Ft Collins, CO 80523 USA Rzeszow Univ Hosp, Ob Gyn Dept, Rzeszow, Poland Univ Leeds, Fac Biol Sci, Leeds, W Yorkshire, England Massachusetts Gen Hosp, Endocrine Unit, Boston, MA 02114 USA Carl von Ossietzky Univ Oldenburg, Neurosci, Oldenburg, Germany UNSW, St George & Sutherland Clin Sch, Microbiome Res Ctr, Sydney, NSW, Australia NYU, Sch Med, Dept Biochem, New York, NY 10016 USA NYU, Sch Med, Dept Mol Pharmacol, New York, NY USA Univ Mississippi, Med Ctr, Med Infect Dis, Jackson, MS 39216 USA Tampere Univ, Fac Social Sci Psychol, Tampere, Finland Univ Cyprus, Dept Biol Sci, Nicosia, Cyprus Goethe Univ, Inst Med Microbiol & Infect Control, Frankfurt, Germany Charite Med Univ Berlin, Inst Radiol, Berlin, Germany Univ Cologne, Univ Hosp Cologne, Internal Med 1, Cologne, Germany Univ Calif Los Angeles, Sch Dent, Sect Periodont, Los Angeles, CA 90024 USA Aalborg Univ Hosp, Dept Clin Biochem, Aalborg, Denmark Manipal Acad Higher Educ, Pharm Practice, Manipal, Karnataka, India Univ Tokyo, Inst Med Sci, Dept Radiol, Tokyo, Japan Cukurova Univ, Family Med, Fac Med, Adana, Turkey Catholic Univ Korea, Coll Med, Dept Humanities & Social Med, Seoul, South Korea Univ Ghent, Food Technol Safety & Hlth, Ghent, Belgium UNSW Sydney, Sch Biol Earth & Environm Sci BEES, Sydney, NSW, Australia St Vincent Shoulder & Sports Clin, Res Unit, Vienna, Austria Cornell Univ, Biomed Engn, Ithaca, NY USA Leibniz Inst Plant Genet & Crop Plant Res IPK, Res Grp Bioinformat & Informat Technol, Gatersleben, Germany Univ Europea Madrid, Sch Doctoral Studies, Madrid, Spain CSIRO Mfg, Biomed Mfg, Melbourne, Vic, Australia Depaul Univ, Biol Sci, Chicago, IL 60604 USA Konkuk Univ, Dept Anim Sci & Technol, Seoul, South Korea Chang Gung Univ, Grad Inst Med Mechatron, Taoyuan, Taiwan Korea Univ, Coll Med, Psychiat, Seoul, South Korea Princeton Univ, Chem, Princeton, NJ 08544 USA Henan Univ Chinese Med, Henan Key Lab Chinese Med Resp Dis, Zhengzhou, Henan, Peoples R China Univ Med Ctr Mainz, Dept Psychiat & Psychotherapy, Mainz, Germany Monash Univ Malaysia, Sch Sci, Selangor, Malaysia Univ Mississippi, Med Ctr, Physiol & Biophys, Jackson, MS 39216 USA Univ Oslo, Dept Transplantat Med, Oslo, Norway Sichuan Agr Univ, Triticeae Res Inst, Yaan, Sichuan, Peoples R China Guangzhou Univ Chinese Med, Gastroenterol, Guangzhou, Guangdong, Peoples R China Southeast Univ, Sch Biol Sci & Med Engn, Suzhou, Jiangsu, Peoples R China Mt Allison Univ, Biol, Sackville, NB, Canada Ithaca Coll, Biol, Ithaca, NY 14850 USA Univ Cagliari, Dept Med Sci & Publ Hlth, Monserrato, Italy Univ Vienna, Chromosome Biol, Vienna, Austria Univ Zurich, Nephrol, Zurich, Switzerland Friedrich Schiller Univ, Inst Nutr Sci, Jena, Germany UNSW Sydney, Grad Sch Biomed Engn, Sydney, NSW, Australia Tulane Univ, Sch Med, Biochem & Mol Biol, 1430 Tulane Ave, New Orleans, LA 70112 USA NINDS, NIH, Bldg 36,Rm 4D04, Bethesda, MD 20892 USA NIH, NCBI, Natl Lib Med, Bldg 10, Bethesda, MD 20892 USA LOreal Res & Innovat, Aulnay Sous Bois, France Lund Univ, Dept Psychol, Malmo, Sweden Catholic Univ Louvain, Inst Rech Expt & Clin, Brussels, Belgium Georgia State Univ, Neurosci Inst, Atlanta, GA 30303 USA Univ Melbourne, Ophthalmol, Surg, Parkville, Vic, Australia Univ Western Australia, Ctr Ophthalmol & Visual Sci, Perth, WA, Australia Iran Univ Med Sci, Med Phys, Fac Med, Tehran, Iran Salk Inst Biol Studies, Cellular Neurobiol, La Jolla, CA USA Imperial Coll London, Fibrosis Res Grp, London, England Univ Texas MD Anderson Canc Ctr, Genitourinary Med Oncol, Houston, TX 77030 USA Univ Liege, GIGA Neurosci, Liege, Belgium Univ Crete, Sch Med, Urol, Iraklion, Greece Flinders Univ S Australia, Flinders Med Ctr, Dept Clin Pharmacol, Adelaide, SA, Australia Max Planck Inst Immunobiol & Epigenet, Bioinformat, Breisgau, Germany Cardiff Univ, Sch Psychol, Cardiff, S Glam, Wales Imperial Coll London, Chem Engn, London, England Lund Univ, Skane Univ Hosp, Clin Sci, Malmo, Sweden Sahlgrens Acad, Inst Clin Sci, Dept Mol & Clin Med, Gothenburg, Sweden Univ Cent Lancashire, Sch Pharm & Biomed Sci, Preston, Lancs, England Hosp Univ Doctor Peset, Psychiat & Clin Psychol, Valencia, Spain Ctr Biol Mol Severo Ochoa, Genome Dynam & Funct, Madrid, Spain Unvivers Hosp Lille, Dept Intens Care, Lille, France Kansai Med Univ, Surg, Osaka, Japan Univ Toulouse, Inst Natl Polytech Toulouse, Ecole Natl Super Agron Toulouse, Lab Genom & Biotechnol Fruit, Toulouse, France UiT Arctic Univ Norway, Inst Psychol, Tromsto, Norway Queens Univ, Ctr Publ Hlth, Belfast, Antrim, North Ireland Univ Manchester, Ctr Primary Care & Hlth Serv Res, Manchester, Lancs, England Griffith Univ, Menzies Hlth Inst, Gold Coast, Qld, Australia Anglia Ruskin Univ, FHSCE, Cambridge, England Univ Lleida, Dept Expt Med, Lleida, Spain NIEHS, Biomol Screening Branch, Div Natl Toxicol Program, POB 12233, Res Triangle Pk, NC 27709 USA Albany Med Coll, Immunol & Microbial Dis, Albany, NY 12208 USA Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia Univ Kent, Sch Biosci, Canterbury, Kent, England Univ Bourgogne Franche Comte, INSERM, LNC, UMR 1231, Besancon, France Ritsumeikan Univ, Coll Life Sci, Dept Biotechnol, Shiga, Japan Kent State Univ, Biol Sci, Kent, OH 44242 USA Natl Inst Infect Dis, Dept Safety Res Blood & Biol Prod, Tokyo, Japan European Inst Marine Studies, Lab Microbiol Extreme Environm, Plouzane, France Univ Iowa, Dept Pharmacol, Roy J & Lucille A Carver Coll Med, Iowa City, IA 52242 USA Natl Univ Singapore, Biol Sci, Singapore, Singapore Conservatoire Natl Arts & Metiers, Lab GBA, EA4627, Paris, France Univ Michigan, Dept Human Genet, Ann Arbor, MI 48109 USA Lomonosov Moscow State Univ, Belozersky Inst Physicochem Biol, Moscow, Russia Jikei Univ, Sch Med, Dept Mol Biol, Tokyo, Japan Univ South Wales, 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Phys & Mech Engn, Brisbane, Qld, Australia Otto von Guericke Univ, Psychol, Magdeburg, Germany Univ Med Ctr Gottingen, Dept Expt Neurodegenerat, Gottingen, Germany Harvard Med Sch, Spaulding Rehabil Hosp, Phys Med & Rehabil, Boston, MA 02115 USA Quadram Inst Biosci, Sci Operat, Norwich, Norfolk, England Ostbayer Tech Hsch Regensburg OTH Regensburg, Regensburg Med Image Comp ReMIC, Regensburg, Germany Deakin Univ, Fac Arts & Educ, Melbourne, Vic, Australia Univ Warwick, Warwick Med Sch, Coventry, W Midlands, England INSERM, Natl Inst Hlth & Med Res, Biochem & Mol Biol, Paris, France Univ Liege, Tax Inst, Liege, Belgium Univ Leeds, Sch Mol & Cellular Biol, Leeds, W Yorkshire, England IRCCS Ist Giannina Gaslini, UOC Genet Med, Genoa, Italy Res Diets Inc, Sci, New Brunswick, NJ USA Univ Perugia, Dept Phys & Geol, Perugia, Italy Walter Reed Natl Mil Med Ctr, Cellular Immunol, Bethesda, MD USA Univ Fed Santa Catarina, Biol Sci Ctr, Microbiol Immunol & Parasitol Dept, Florianopolis, SC, Brazil Univ Edinburgh, Royal Infirm, Ctr Liver & Digest Disorders, Edinburgh, Midlothian, Scotland Orion Pharma, Crit Care Proprietary Prod Div, Espoo, Finland MIT, Dept Civil & Environm Engn, 77 Massachusetts Ave, Cambridge, MA 02139 USA Univ Turin, Dept Vet Sci, Turin, Italy Univ G dAnnunzio, Dept Psychol Hlth & Territorial Sci, Chieti, Italy NYU, Sch Med, OB GYN, New York, NY USA Univ Glasgow, Inst Cardiovasc & Med Sci, Glasgow, Lanark, Scotland Univ Turku, Dept Biol, Turku, Finland Tech Univ Berlin, Bioanalyt, Berlin, Germany Univ Goettingen, Inst Phys Biophys 3, Gottingen, Germany Univ Texas MD Anderson Canc Ctr, Inst Appl Canc Sci, Translat Res Adv Therapeut & Innovat Oncol TRACTI, Houston, TX 77030 USA Univ Liege, Life Sci, Liege, Belgium Tarbiat Modares Univ, Fac Med Sci, Dept Toxicol, Tehran, Iran ARS, USDA, Stoneville, MS USA Univ Regensburg, RCI Regensburg Ctr Intervent Immunol, Regensburg, Germany Univ Nottingham, Sch Psychol, Nottingham, England NIH, Pathol Lab, Bethesda, MD 20892 USA Univ Carlos III Madrid, Elect Engn, Madrid, Spain Inst Med Mol, Chem Biol, Lisbon, Portugal Univ Costa Rica, CIET, San Jose, Costa Rica Univ Stavanger, Fac Hlth Sci, Stavanger, Norway Erasmus MC, Urol, Rotterdam, Netherlands Univ Edinburgh, Sch Biol Sci, Edinburgh, Midlothian, Scotland German Res Ctr Environm Hlth GmbH, Helmholtz Zentrum Munchen, Inst Bioinformat & Syst Biol IBIS, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany Leiden Univ, Huygens Kamerlingh Onnes Lab, Leiden, Netherlands Univ Vienna, Nutr Sci, Vienna, Austria Kolling Inst Med Res, Med, St Leonards, NSW, Australia Johns Hopkins Sch Med, Biol Chem, Baltimore, MD USA Univ Montreal, Med Nutr & Microbiome Lab, Montreal, PQ, Canada GlaxoSmithKline, Cell & Gene Therapy, Stevenage, Herts, England Univ Trieste, Life Sci, Trieste, Italy Rhein Westfal TH Aachen, Dept Radiol, Aachen, Germany Univ Duisburg Essen, Univ Hosp Essen, West German Canc Ctr, Dept Med Oncol, Essen, Germany Med Univ Vienna, Obstet & Gynecol, Vienna, Austria FHI 360, Social & Behav Hlth Sci Div, Washington, DC USA KU Leuven VIB, Switch Lab, Leuven, Belgium Bielefeld Univ, Fac Technol, Bielefeld, Germany Capital Med Univ, Beijing Shijitan Hosp, Dept Clin Nutr, Dept Gastrointestinal Surg, Beijing, Peoples R China Meiji Univ, Dept Agr Chem, Kawasaki, Japan Yonsei Univ, Coll Med, Microbiol, Seoul, South Korea Johnson & Johnson EAME, Maidenhead, Berks, England Penn State Coll Med, Pediat, Hershey, PA USA Univ N Carolina, Dept Social Med, Chapel Hill, NC 27515 USA Univ Western Australia, ARC CoE Plant Energy Biol, Perth, WA, Australia Wageningen Univ, Div Human Nutr & Hlth, Wageningen, Netherlands Kings Coll London, Dept Neuroimaging, London, England Univ Murcia, Biochem & Mol Biol, Murcia, Spain Old Dominion Univ, Dept Biol Sci, Norfolk, VA 23529 USA Monash Univ, Biochem & Mol Biol, Melbourne, Vic, Australia Chinese Acad Sci, Inst Genet & Dev Biol, Beijing, Peoples R China Univ Pittsburgh, Pharmacol & Chem Biol, Pittsburgh, PA USA Univ Lausanne, Ctr Integrat Genom, Lausanne, Switzerland Univ Queensland, Sch Pharm, Brisbane, Qld, Australia Leibniz Inst Plant Genet & Crop Plant Res IPK Gat, Genebank, Gatersleben, Germany Piramal Imaging, Res & Dev, Berlin, Germany Univ Leeds, Civil Engn, Leeds, W Yorkshire, England Univ Missouri, Chem & Biochem, St Louis, MO 63121 USA US Geol Survey, Coastal & Marine Geol Program, Pacific Coastal & Marine Sci Ctr, Santa Cruz, CA USA Ajinomoto Genet Res Inst, Moscow, Russia Jagiellonian Univ, Fac Biochem Biophys & Biotechnol, Dept Plant Biotechnol, Krakow, Poland Univ Puerto Rico, Engn Sci & Mat, Mayaguez, PR USA Univ Regina, Dept Chem & Biochem, Regina, SK, Canada Argonne Natl Lab, Ctr Nanoscale Mat, 9700 S Cass Ave, Argonne, IL 60439 USA Univ Sunshine Coast, Sunshine Coast Mind & Neurosci Thompson Inst, Sippy Downs, Qld, Australia Chinese Peoples Liberat Army Gen Hosp, Dept Gastroenterol & Hepatol, Beijing, Peoples R China Griffith Univ, Griffith Ctr Social & 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Maximilians Univ Munchen, Phys Chem, NanoBioSci, Munich, Germany Bandung Inst Technol, Sch Pharm, Med Chem, Bandung, Indonesia Univ Luxembourg, Life Sci Res Unit, Luxembourg, Luxembourg Lund Univ, Skane Univ Hosp, Dept Gastroenterol, Malmo, Sweden Millennium Hlth, Translat Genet, San Diego, CA USA Aristotle Univ Thessaloniki, Med Dept 2, Clin Res & Evidence Based Med Unit, Thessaloniki, Greece Jan Kochanowski Univ Humanities & Sci, Piotrkow Trybunalski Branch, Dept Psychol, Kielce, Poland McMaster Univ, Engn Phys, Hamilton, ON, Canada Marche Polytech Univ, Dept Agr Food & Environm Sci, Ancona, Italy Kuwait Univ, Fac Med, Microbiol, Kuwait, Kuwait Fujita Hlth Univ, Dept Breast Surg, Toyoake, Aich, Japan North West Reg Spinal Injuries Ctr, Spinal Injuries Ctr, Southport, Merseyside, England Luxembourg Inst Hlth, Competence Ctr Methodol & Stat, Luxembourg, Luxembourg Nestle Inst Hlth Sci SA, Metab Hlth, Ecublens, Vaud, Switzerland Ctr Inflammat Res VIB, Ghent, Belgium Univ Ghent, Dept Biomed Mol Biol, Ghent, Belgium Univ Lisbon, Inst Educ, Curriculo Formacao Prof & Tecnol, Lisbon, Portugal Univ Edinburgh, Ctr Inflammat Res, Edinburgh, Midlothian, Scotland Univ Melbourne, Sch BioSci, Parkville, Vic, Australia Northumbria Univ, Comp & Informat Sci, Newcastle Upon Tyne, Tyne & Wear, England Univ Valencia, Endocrinol, Valencia, Spain INRS, Inst Armand Frappier, Laval, PQ, Canada Univ Laval, INAF, Sch Nutr, Quebec City, PQ, Canada Univ Konstanz, Dept Biol, Constance, Germany Univ Cote dAzur, LAMHESS, Nice, France Scion, Syst Ecol, Christchurch, New Zealand CUNY, Grad Sch Publ Hlth & Hlth Policy, Epidemiol & Biostat, New York, NY 10021 USA Univ Queensland, Sch Dent, Brisbane, Qld, Australia George Inst Global Hlth, Renal & Metab Div, Sydney, NSW, Australia Wuhan Univ, Coll Chem & Mol Sci, Wuhan, Hubei, Peoples R China Griffith Univ, Sch Environm & Sci, Gold Coast, Qld, Australia Univ Minnesota, Radiat Oncol, Minneapolis, MN USA Goethe Univ, Fac Med, Frankfurt, Germany Natl Yunlin Univ Sci & Technol, Dept & Grad Sch Safety & Environm Engn, Touliu, Yunlin, Taiwan Massey Univ, Sch Sport Exercise & Nutr, Auckland, New Zealand Univ Florida, Wildlife Ecol & Conservat, Gainesville, FL USA Bournemouth Univ, Dept Psychol, Poole, Dorset, England Robert Koch Inst, Project Grp P2, Berlin, Germany Univ Edinburgh, MRC Inst Genet & Mol Med, Edinburgh, Midlothian, Scotland Univ Basel, Biozentrum, Basel, Switzerland Univ Wollongong, Sch Med, Wollongong, NSW, Australia Univ Cologne, Inst Human Genet, Cologne, Germany Rural Econ Branch, Econ Res Serv, Washington, DC USA Uivers Bordeaux, CNRS, Inst Neurosci Cognit & Integrat Aquitaine, Bordeaux, France Univ Calif Riverside, Dept Chem & Environm Engn, Riverside, CA 92521 USA Univ Calif Riverside, Mat Sci & Engn Program, Riverside, CA 92521 USA Mackay Med Coll, Dept Med, New Taipei, Taiwan Univ Bern, Div Anim Welf, Bern, Switzerland Oregon Hlth & Sci Univ, Dept Physiol & Pharmacol, Portland, OR 97201 USA Shanghai Univ Tradit Chinese Med, Inst Interdisciplinary Med Sci, Shanghai, Peoples R China McMaster Univ, Biol, Hamilton, ON, Canada Univ S Florida, Dept Cell Biol Microbiol & Mol Biol, Tampa, FL USA Hacettepe Univ, Inst Canc, Med Oncol, Ankara, Turkey City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China Natl Taiwan Univ, Dept Entomol, Taipei, Taiwan Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Ecol Secur, Beijing, Peoples R China Florida State Univ, Inst Mol Biophys, Chem & Biochem, Tallahassee, FL USA Peking Univ, Shenzhen Grad Sch, State Key Lab Chem Oncogen, Lab Computat Chem & Drug Design, Shenzhen, Peoples R China Univ Helsinki, Fac Pharm, Div Pharmaceut Chem & Technol, Drug Res Program, Helsinki, Finland Tohoku Univ, Microbial Biotechnol, Sendai, Miyagi, Japan Tianjin Med Univ, Sch Basical Med Sci, Dept Pharmacol, Tianjin, Peoples R China Dana Farber Canc Inst, Biostat & Computat Biol, Boston, MA 02115 USA Natl Hlth Res Inst, Inst Mol & Genom Med, Zhunan, Taiwan Univ Oxford, Physiol Anat & Genet, Oxford, England George Washington Univ, Phys, Washington, DC USA Univ Nebraska, Sch Biol Sci, Lincoln, NE USA Toronto Gen Hosp, Res Inst, Dept Lab Med & Pathobiol, Toronto, ON, Canada Univ Texas Dallas, Biol Sci, Richardson, TX 75083 USA NYU, Dept Chem, New York, NY USA Shandong Univ, Sch Math & Stat, Jinan, Shandong, Peoples R China Univ Sci & Technol China, Hefei Natl Lab Phys Sci Microscale, Hefei, Anhui, Peoples R China Purdue Univ, Med Chem & Mol Pharmacol, W Lafayette, IN 47907 USA NIBSC, Adv Therapies, Ridge, Herts, England Shanghai Jiao Tong Univ, Ruijin Hosp, Dept Pulm & Crit Care Med, Shanghai, Peoples R China Charite Med Univ Berlin, Dermatol & Allergy, Berlin, Germany Univ Hosp St Etienne, Hematol, St Etienne, France Inland Norway Univ Appl Sci, Inst Biotechnol, Elverum, Norway Univ Jordan, Pediat, Amman, Jordan Inst Pasteur, Mol Mycol Unit, Paris, France Cardiff Univ, Sch Med, Inst Psychol Med & Clin Neurosci, Med Res Council Ctr Neuropsychiat Genet & Genom, Cardiff, S Glam, Wales Zurich Univ Appl Sci, Social Work, Zurich, Switzerland Jawaharlal Nehru Univ, Sch Life Sci, New Delhi, India Univ Burgundy Franche Comte, LE2I, Dijon, France Univ Roehampton, Life Sci, London, England Ghent Univ Hosp, Gen & HPB Surg, Ghent, Belgium Univ Wurzburg, Insect Fungus Symbiosis Lab, Wurzburg, Germany Radboud Univ Nijmegen, Behav Sci Inst, Nijmegen, Netherlands Fraunhofer WKI, Applicat Ctr HOFZET, Hannover, Germany UCL, Struct & Mol Biol, London, England Univ Amsterdam, Dev Psychol, Amsterdam, Netherlands Aalborg Univ, Hlth Sci & Technol, CNAP, SMI, Aalborg, Denmark VA Pittsburgh Healthcare Syst, Ctr Hlth Equity Res & Promot, Pittsburgh, PA USA Cedars Sinai Med Ctr, Neurosurg, Los Angeles, CA 90048 USA Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou, Guangdong, Peoples R China Dezhou Univ, Shandong Prov Key Lab Biophys, Guangzhou, Guangdong, Peoples R China Maison Teledetection, Inst Rech Dev, UMR Espace DEv, Montpellier, France Xiamen Univ, Sch Life Sci, Xiamen, Fujian, Peoples R China Nanjing Univ, Sch Med, Jinling Hosp, Natl Clin Res Ctr Kidney Dis,Dept Med Imaging, Nanjing, Jiangsu, Peoples R China Univ Kent, Sch Social Policy Sociol & Social Res, Canterbury, Kent, England Univ Fed Minas Gerais, Infect Dis & Trop Med, Belo Horizonte, MG, Brazil Univ Minho, Sch Med, Life & Hlth Sci Res Inst ICVS, Braga, Portugal Univ Montreal, Biochim & Med Mol, Montreal, PQ, Canada Johns Hopkins Bloomberg Sch Publ Hlth, Epidemiol, Baltimore, MD USA Max Planck Inst Biol Ageing, Metab & Genet Regulat Ageing, Cologne, Germany Univ Swaziland, Hlth Sci, Kwaluseni, Eswatini Queensland Univ Technol, Inst Future Environm, Brisbane, Qld, Australia Ctr Sci Monaco, Dept Biol Med, Monaco, Monaco HELIOS Hosp, Urol, Bad Saarow Pieskow, Germany Tech Univ Carolo Wilhelmina Braunschweig, Inst Microbiol, Braunschweig, Germany Univ Barcelona, Barcelona Ctr Maternal Fetal & Neonatal Med, Fetal i D Fetal Med Res Ctr, IDIBAPS BCNatal,Hosp Clin, Barcelona, Spain Univ Barcelona, Hosp St Joan de Deu, Barcelona, Spain Friedrich Loeffler Inst, Inst Bacterial Infect & Zoonoses, Jena, Germany Charite Med Univ Berlin, Neurol, Berlin, Germany Dublin City Univ, Natl Inst Cellular Biotechnol, Mol Therapeut Canc Ireland, Dublin, Ireland Schoen Clin Roseneck, Prien Am Chiemsee, Germany Univ Med Ctr Hamburg Eppendorf, Inst Sex Res & Forens Psychiat, Hamburg, Germany Nankai Univ, Sch Math Sci, Tianjin, Peoples R China Nankai Univ, LPMC, Tianjin, Peoples R China Univ Oxford, Oncol, Oxford, England Royal Holloway Univ London, Class, Egham, Surrey, England Cornell Univ, Clin Sci, Ithaca, NY USA Univ KwaZulu Natal, Pharmaceut Chem, Westville Campus, Durban, South Africa Royal Coll Surgeons Ireland, Med, Dublin, Ireland Univ Oslo, Dept Immunol, Oslo, Norway Bermuda Inst Ocean Sci, Marine Nitrogen Cycling Lab, St Georges, Bermuda Kanazawa Univ, Inst Liberal Arts & Sci, Kanazawa, Ishikawa, Japan World Hlth Org Reg Off Africa, Brazzaville, Rep Congo Univ Hosp BesanCon, Infect Control Dept, Besancon, France Galapagos NV, Clin Dev, Mechelen, Belgium Univ Tasmania, Integrated Marine Observing Syst, Hobart, Tas, Australia Georg August Univ Gottingen, Albrecht von Haller Inst Plant Sci, Dept Systemat Biodivers & Evolut Plants, Gottingen, Germany Univ Occupat & Environm Hlth, Dept Psychiat, Fukuoka, Fukuoka, Japan IMDEA Food, Program Precis Nutr & Aging, Madrid, Spain Radboud Univ Nijmegen, Med Sch, IQHealthcare, Nijmegen, Netherlands Maastricht Univ, Dept Cardiovasc Surg, Maastricht, Netherlands German Diabet Ctr, Inst Clin Biochem & Pathobiochem, Dusseldorf, Germany Juntendo Univ, Grad Sch Med, Dept Radiol, Tokyo, Japan Deakin Univ, Sch Informat Technol, Melbourne, Vic, Australia Max Planck Inst Eusenforschung, Dept Interface Chem & Surface Sci, Dusseldorf, Germany Edge Hill Univ, Dept Psychol, Ormskirk, England Aga Khan Univ, Psychiat, Karachi, Pakistan KRIBB, Korean Bioinformat Ctr, Seoul, South Korea Cardinal Hlth Specialty Solut, Hlth Econ & Outcomes Res, Dallas, TX USA Klinikum Univ Munchen, Div Clin Pharmacol, Munich, Germany Univ Pittsburgh, Neurol Surg, Pittsburgh, PA USA Rhein Westfal TH Aachen, Dept Child & Adolescent Psychiat Psychosomat & Ps, Aachen, Germany Univ Copenhagen, Inst Mol & Cellular Biol, Copenhagen, Denmark St Jude Childrens Res Hosp, Struct Biol, 332 N Lauderdale St, Memphis, TN 38105 USA Royal Shrewsbury Hosp, Colorectal Surg, Shrewsbury, Salop, England Univ Nottingham, Fac Med & Hlth Sci, Nottingham, England Karolinska Inst, Dept Physiol & Pharmacol, Solna, Sweden Chinese Acad Sci, Changchun Inst Appl Chem, State Key Lab Electroanalyt Chem, Jilin, Jilin, Peoples R China Univ British Columbia, Pediat, Vancouver, BC, Canada Chinese Acad Agr Sci, State Key Lab Cotton Biol, Res Base Anyang Inst Technol, Cotton Germplasm Resources,Inst Cotton Res, Beijing, Peoples R China Chinese Univ Hong Kong, Anaesthesia & Intens Care, Hong Kong, Peoples R China Univ Macau, ICMS, Zhuhai, Guangdong, Peoples R China North China Elect Power Univ, Sch Renewable Energy, Beijing, Peoples R China Justus Liegbig Univ, Dept Internal Med, Giessen, Germany Aarhus Univ, Biosci, Aarhus, Denmark Univ Dublin, Trinity Coll Dublin, Irish Longitudinal Study Ageing TILDA, Dublin, Ireland Univ Groningen, Univ Med Ctr Groningen, Hematol, Groningen, Netherlands Vrije Univ Amsterdam Med Ctr, Child Neurol, Amsterdam, Netherlands EBI, EMBL, Cambridge, England Max Planck Inst Marine Microbiol, HGF MPG Joint Res Grp Deep Sea Ecol & Technol, Bremen, Germany Max Planck Inst Human Dev, Ctr Adapt Rat, Berlin, Germany King Faisal Univ, Math, Al Hufuf, Saudi Arabia Griffith Univ, Sch Nursing & Midwifery, Gold Coast, Qld, Australia Iowa State Univ, Roy J Carver Dept Biochemsitry Biophys & Mol Biol, Ames, IA USA Delft Univ Technol, Fac Mech Maritime & Mat Engn, Engn Thermodynam Proc & Energy Dept, Leeghwaterstr 39, NL-2628 CB Delft, Netherlands Univ Nebraska Med Ctr, Coll Allied Hlth Profess, Cytotechnol Educ, Omaha, NE USA Shinko Mem Hosp, Dept Cardiovasc Med, Kobe, Hyogo, Japan Imperial Coll London, Mat, London, England Tech Univ Munich, Dept Surg, Munich, Germany Chinese Acad Agr Sci, Res Inst Pomol, Minist Agr, Lab Qual & Safety Risk Assessment Fruit Xingcheng, Shenyang, Liaoning, Peoples R China James Madison Univ, Commun Sci & Disorders, Harrisonburg, VA 22807 USA Univ Hosp Ulm, Inst Orthopaed Res & Biomech, Ulm, Germany Univ Essex, Sch Hlth & Social Care, Colchester, Essex, England Alpha Altis, Res Serv, Nottingham, England Erasmus MC, Med Oncol, Rotterdam, Netherlands Fed Univ Oye, Dept Ind Chem, Ekiti, Nigeria Duke Univ, Med Ctr, Cell Biol, Durham, NC USA Univ Oxford, Nuffield Dept Clin Neurosci, Oxford, England Univ Manchester, Canc Res UK Manchester Inst, Manchester, Lancs, England Helsinki Univ Hosp, Childrens Hosp, Helsinki, Finland Univ Aveiro, CESAM Ctr Environm & Marine Studies, Dept Biol, Aveiro, Portugal Univ Botswana, Psychol, Gaborone, Botswana Univ Fed Bahia, Nursing Sch, Salvador, BA, Brazil Queen Mary Univ London, Biol & Expt Psychol, London, England Natl Univ Pharm, Med Chem Dept, Kharkov, Ukraine Univ Bolton, Dept Educ & Psychol, Bolton, England La Trobe Univ, Dept Chem & Phys, Melbourne, Vic, Australia Gen Hosp Northern Theater Command, Dept Gastroenterol, Shenyang, Liaoning, Peoples R China Doctors Hosp, Dept Nephrol, Athens, Greece Univ Hosp Essen, Pediat 3, Essen, Germany Imperial Coll London, Infect Dis Epidemiol, London, England Sorbonne Univ, Dept Psychiat, Paris, France UNSW Sydney, Educ, Sydney, NSW, Australia Stanford Univ, Dept Psychiat & Behav Sci, Palo Alto, CA 94304 USA Hannover Med Sch, Clin Laryngol Rhinol & Otol, Hannover, Germany Curtin Univ, Ctr Aboriginal Studies, Perth, WA, Australia Iran Univ Sci & Technol, Biomed Engn Dept, Tehran, Iran Univ Calif San Francisco, Anesthesiol, San Francisco, CA 94143 USA Khalifa Univ Sci & Technol, Mech Engn, Abu Dhabi, U Arab Emirates Univ Florida, Hort Sci, Gainesville, FL USA James Cook Univ, Australian Inst Trop Hlth & Med, Ctr Biodiscovery & Mol Dev Therapeut, Cairns, Qld, Australia Univ Porto, Fac Med, CINTESIS, Porto, Portugal Shaoxing Peoples Hosp, Med Res Ctr, Shaoxing, Zhejiang, Peoples R China NIH, Dept Transfus Med, Bethesda, MD 20892 USA AIIMS, Dept Biotechnol, New Delhi, India Univ Ottawa, Biochem Microbiol & Immunol, Ottawa, ON, Canada Univ Oslo, Inst Clin Med, Div Mental Hlth & Addict, Oslo, Norway Univ Groningen, Univ Med Ctr Groningen, Dept Radiol, Groningen, Netherlands Univ Hong Kong, Sch Nursing, Hong Kong, Peoples R China Tokyo Med Univ, Ibaraki Med Ctr, Urol, Tokyo, Japan Univ Hosp Zurich, Dept Radiat Oncol, Zurich, Switzerland Univ Maryland, Inst Human Virol, Div Immunotherapy, Baltimore, MD 21201 USA Univ Maryland, Dept Surg, Baltimore, MD 21201 USA Stellenbosch Univ, Fac Med & Hlth Sci, Div Mol Biol & Human Genet, Stellenbosch, South Africa China Agr Univ, Coll Biol Sci, Beijing, Peoples R China Osaka Univ, Grad Sch Pharmaceut 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Eccles Inst Neurosci, John Curtain Sch Med Res, Canberra, ACT, Australia John Innes Ctr, Metab Biol, Norwich, Norfolk, England USDA ARS, Genom & Bioinformat Res Unit, Raleigh, NC 27695 USA Med Univ Graz, Inst Med Informat Stat & Documentat, Holzinger Grp, Graz, Austria Ajou Univ, Pharm, Suwon, South Korea City Univ Hong Kong, Sch Energy & Environm, Hong Kong, Peoples R China Univ British Columbia, Sch Kinseiol, Vancouver, BC, Canada Univ Copenhagen, Marine Biol Sect, Dept Biol, Copenhagen, Denmark Univ Vienna, Dept Commun, Vienna, Austria Univ Dundee, Sch Social Sci, Dundee, Scotland Tech Univ Dresden, Inst Bot, Dresden, Germany Univ Oxford, Div Struct Biol, Oxford, England Natl Univ Hlth Syst, Med, Singapore, Singapore Univ Canterbury, Sch Biol Sci, Christchurch, New Zealand Univ Hosp Southern Denmark, Focused Res Unit Mol Diagnost & Clin Res, Odense, Denmark Univ Oxford, Primary Care Hlth Sci, Oxford, England Baylor Coll Med, Verna & Marrs McLean Dept Biochem & Mol Biol, Houston, TX 77030 USA Adnan Menderes Univ Aydin, Fac Nursing, Dept Publ Hlth Nursing, Aydin, Turkey Oasi Res Inst IRCCS, Dept Neurol IC, Troina, Italy Purdue Univ, Weldon Sch Biomed Engn, W Lafayette, IN 47907 USA Kings Coll London, Kings Ctr Mil Hlth Res, London, England LSHTM, Dept Infect Dis Epidemiol, London, England Leibniz Univ Hannover, BMWZ Organ Chem, Hannover, Germany Xi An Jiao Tong Univ, Sch Basic Med Sci, Dept Physiol & Pathophysiol, Xian, Shaanxi, Peoples R China Univ South Australia, Sch Pharm & Med Sci, Adelaide, SA, Australia Univ Fed Santa Catarina, Dept Phys Educ, Florianopolis, SC, Brazil Southern Med Univ, Nanfang Hosp, Dept Oncol, Guangzhou, Guangdong, Peoples R China Stanford Univ, Hansen Expt Phys Lab, Palo Alto, CA 94304 USA Shenzhen Univ, Affiliated Hosp 1, Shenzhen Peoples Hosp 2, Inst Translat Med, Shenzhen, Guangdong, Peoples R China Univ Hong Kong, Dept Stat & Actuarial Sci, Hong Kong, Peoples R China UCL, Dept Mech Engn, London, England ASTAR, Singapore Immunol Network, Lab Microbial Immun, Singapore, Singapore Cent South Univ, State Key Lab Powder Met, Changsha, Hunan, Peoples R China Univ Aberdeen, Inst Appl Hlth Sci, Aberdeen, Scotland Univ Bridgeport, Biomed Engn, Bridgeport, CT 06601 USA Texas Tech Univ, Hlth Sci Ctr, Pharmaceut Sci, Lubbock, TX 79430 USA Univ Montana, Ecosyst & Conservat Sci, Missoula, MT 59812 USA Univ Goettingen, Dept Syst Neurosci, Gottingen, Germany NHLBI, Lab Syst Genet, Bldg 10, Bethesda, MD 20892 USA Cleveland Clin, Lou Ruvo Ctr Brain Hlth, Imaging, Las Vegas, NV USA Flinders Univ S Australia, Coll Nursing & Hlth Sci, Nutr & Dietet, Adelaide, SA, Australia Univ Padua, Dept Math, Padua, Italy Lund Univ, Fac Law, Lund, Sweden Univ Gothenburg, Dept Microbiol & Immunol, Gothenburg, Sweden NARO, Kachwekano Zardi, Entebbe, Uganda Natl Yunlin Univ Sci & Technol, Bachelor Program Interdisciplinary Studies, Touliu, Yunlin, Taiwan Aarhus Univ, Dept Biomed, Danish Res Inst Translat Neurosci DANDRITE, Aarhus, Denmark Eduardo Mondlane Univ, Math & Comp Sci, Maputo, Mozambique Univ Bern, Dept Old Age Psychiat & Psychotherapy, Bern, Switzerland RAS, Inst Cytol, Lab Cytol Unicellular Organisms, St Petersburg, Russia Beijing Inst Technol, Sch Chem & Chem Engn, Beijing, Peoples R China Univ Queensland, Queensland Alliance Agr & Food Innovat, Brisbane, Qld, Australia Fraunhofer Inst Toxicol & Expt Med ITEM, Inhalat Toxicol, Hannover, Germany Univ Hong Kong, Publ Hlth, Hong Kong, Peoples R China Univ Hlth Network, Anesthesia & Pain Med, Toronto, ON, Canada Univ Toronto, Toronto, ON, Canada Univ Bath, Dept Hlth, Bath, Avon, England Univ Copenhagen, Computat & RNA Biol, Copenhagen, Denmark Fisheries & Oceans Canada, Bedford Inst Oceanog, Dartmouth, NS, Canada Goethe Univ, CEF MC, BMLS, Phys Biol, Frankfurt, Germany Albert Einstein Coll Med, Anat & Struct Biol, New York, NY USA Queensland Govt, Dept Environm & Sci, Brisbane, Qld, Australia Uppsala Univ, Vasc Surg Sect, Dept Surg Sci, Uppsala, Sweden Childrens Canc Hosp, Res, Cairo, Egypt Leibniz Inst Nat Prod Res & Infect Biol, Bio Pilot Plant, Jena, Germany Duy Tan Univ, Inst Res & Dev, Da Nang, Vietnam Univ Helsinki, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland Univ Queensland, Australian Inst Bioengn & Nanotechnol, Brisbane, Qld, Australia George Inst Global Hlth, Sydney, NSW, Australia Griffith Univ, Griffith Inst Drug Discovery, Brisbane, Qld, Australia Dezhou Univ, Coll Phys & Elect Informat, Shandong Prov Key Lab Biophys, Dezhou, Peoples R China Henan Agr Univ, Coll Life Sci, Zhengzhou, Henan, Peoples R China Univ Tokyo, Publ Hlth, Tokyo, Japan Sun Yat Sen Univ, Affiliated Hosp 1, Guangzhou, Guangdong, Peoples R China Univ Illinois, Dept Med, Chicago, IL USA Beijing Inst Microbiol & Epidemiol, State Key Lab Pathogen & Biosecur, Beijing, Peoples R China Minist Hlth, Key Lab Neonatal Dis, Shanghai, Peoples R China Covenant Univ, Dept Phys, Ota, Nigeria Prince Sattam Bin Abdulaziz Univ, Dept Phys Therapy & Hlth Rehabil, Al Kharj, Saudi Arabia Lund Univ, Cognit Sci, Malmo, Sweden Natl Open Univ Nigeria, Dept Publ & Environm Hlth, Abuja, Nigeria Peking Univ, Sch Publ Hlth, Dept Lab Sci & Technol, Beijing, Peoples R China Univ Sydney, Sch Publ Hlth, Menzies Ctr Hlth Policy, Sydney, NSW, Australia Univ Auckland, Dept Elect & Comp Engn, Auckland, New Zealand Beijing Univ Chinese Med, Res Ctr TCM Informat Engn, Beijing, Peoples R China Osped Niguarda Ca Granda, Cardiac Surg, Milan, Italy Univ Vet Med, Clin Horses, Hannover, Germany Harbin Med Univ, Lab Med Genet, Harbin, Heilongjiang, Peoples R China Univ Saskatchewan, Dept Psychol, Saskatoon, SK, Canada Univ Coimbra, Ctr Studies Geog & Spatial Planning CEGOT, Coimbra, Portugal Univ Groningen, Univ Med Ctr Groningen, Epidemiol, Groningen, Netherlands South Cent High Specialty Hosp, Dept Neurol & Neurosurg, Pemex, Mexico Shandong Agr Univ, Coll Informat Sci & Engn, Tai An, Shandong, Peoples R China Curtin Univ, Natl Drug Res Inst, Perth, WA, Australia Wageningen Bioveterinary Res, Bacteriol & Epidemiol, Lelystad, Netherlands Guangdong Second Prov Gen Hosp, Dept Rheumatol & Immunol, Guangzhou, Guangdong, Peoples R China Erasmus MC, Biomed Rngineering, Rotterdam, Netherlands Hiroshima Univ, Grad Sch Biomed & Hlth Sci, Hiroshima, Japan Univ Iceland, Sch Hlth Sci, Reykjavik, Iceland Ohio State Univ, Mat Sci & Engn, Columbus, OH 43210 USA Kathmandu Univ, Sch Med Sci, Dept Physiotherapy, Dhulikhel, Nepal Univ Queensland, Sch Biomed Sci, Brisbane, Qld, Australia Fraunhofer MEVIS, Image Guided Therapies, Bremen, Germany Natl Univ Hlth Syst, Haematol Oncol, Singapore, Singapore Sun Yat Sen Univ, Canc Ctr, Breast Oncol, Guangzhou, Guangdong, Peoples R China Med Coll Wisconsin, Pharmacol & Toxicol, Wauwatosa, WI USA Queensland Univ Technol, Sci & Engn Fac, Sch Chem Phys & Mech Engn, Brisbane, Qld, Australia Univ Turin, Dept Mol Biotechnol & Hlth Sci, Turin, Italy Univ Tehran Med Sci, Sch Rehabil, Physiotherapy Dept, Tehran, Iran Univ Helsinki, Dept Forest Sci, Helsinki, Finland Univ Messina, Human Pathol, Messina, Italy AO Papardo Hosp Messina, Messina, Italy Univ Ibadan, Coll Med, Inst Child Hlth, Ibadan, Nigeria King Faisal Univ, Coll Med, Fac Ophthalmol, Al Hasa, Saudi Arabia Univ Stirling, Inst Social Mkt, Stirling, Scotland Saveh Univ Med Sci, Social Determinants Hlth Res Ctr, Saveh, Iran Gakujutsu Shien Co Ltd, Tokyo, Japan Chinese Acad Sci, Inst Geochem, Guiyang, Guizhou, Peoples R China Univ Plymouth, Med Sch, Plymouth, Devon, England CHU Toulouse, Immunol, Toulouse, France Azorean Biodivers Grp, Ctr Ecol Evolut & Environm Changes, Azores, Portugal Univ Acores, Azores, Portugal RIKEN, Ctr Integrat Med Sci, Yokohama, Kanagawa, Japan Peking Univ, Sch Publ Hlth, Dept Global Hlth, Beijing, Peoples R China Chang Gung Univ, Chang Gung Mem Hosp, Dept Neurol, Linkou Med Ctr, Taoyuan, Taiwan Chang Gung Univ, Coll Med, Taoyuan, Taiwan Univ Malawi, Coll Med, Biomed Sci Dept, Blantyre, Malawi Univ Malawi, Coll Med, Pharm Dept, Blantyre, Malawi Bioself Commun, Biocurat, Marseille, France Peking Univ, Hosp 3, Dept Neurol, Beijing, Peoples R China Ahmadu Bello Univ, Fac Basic Clin Sci, Coll Hlth Sci, Dept Pathol, Zaria, Nigeria Dalhousie Univ, Dept Anesthesia Pain Management & Perioperat Med, Halifax, NS, Canada VisMederi Srl, Siena, Italy UCL, Canc Res UK, London, England UCL, UCL Canc Trials Ctr, London, England Univ Ottawa, Family Med, Ottawa, ON, Canada China Agr Univ, Coll Engn, Beijing, Peoples R China Leiden Univ, Leiden Acad Ctr Drug Res, Div Drug Discovery & Safety, Leiden, Netherlands Sun Yat Sen Univ, Affiliated Hosp 1, Dept Intervent Radiol, Guangzhou, Guangdong, Peoples R China Amer Univ Beirut, Med Ctr, Infect Dis, Beirut, Lebanon Sheffield Hallam Univ, Dept Social Work Social Care & Community Studies, Sheffield, S Yorkshire, England Mechnikov Res Inst Vaccines & Sera, Viral Hepatitis, Moscow, Russia Univ Ottawa, Pediat, Ottawa, ON, Canada Vreden Russian Res Inst Traumatol & Orthopaed, Dept Wound Infect Treatment & Prevent, St Petersburg, Russia Hangzhou Ctr Dis Control & Prevent, Dept TB Control & Prevent, Hangzhou, Zhejiang, Peoples R China Kaohsiung Med Univ, Dept Biotechnol, Kaohsiung, Taiwan Zoetis, Diagnost, Kalamazoo, MI USA Aintree Univ Hosp NHS Fdn Trust, Head & Neck Oncol Res, Liverpool, Merseyside, England Wrightington Hosp, Trauma & Orthopaed, Manchester, Lancs, England Loyola Univ, Med Ctr, Dept Psychiat, 2160 S 1st Ave, Maywood, IL 60153 USA Atkins Vet Serv, Microbiol, Calgary, AB, Canada Univ Porto, FADEUP, CIAFEL, Porto, Portugal Natl Univ Singapore, Saw Swee Hock Sch Publ Hlth, Singapore, Singapore Kangwon Natl Univ, Coll Biotechnol & Biosci, Dept Food Sci & Biotechnol, Chunchon, South Korea Kakatiya Med Coll, Internal Med, Warangal, Telangana, India Univ Antioquia, Vet Med Sch, CIBAV Res Grp, Medellin, Colombia IISER, Dept Phys, Soft & Act Matter Grp, Tirupati 517507, Andhra Pradesh, India Univ Rosario, Sch Med & Hlth, Ctr Studies Phys Activ Measurements, 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Informat Engn, Xiangtan, Hunan, Peoples R China Univ Hlth Network, Mood Disorders & Psychopharmacol, Toronto, ON, Canada Sao Paulo State Univ UNESP, Dept Anim Sci, Sao Paulo, Brazil Sao Paulo State Univ, Vet Clin, Sao Paulo, Brazil

International audience

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

In this paper, we study the problem of discovering the Markov blanket (MB) of a target variable from multiple interventional datasets. Datasets attained from interventional experiments contain richer causal information than passively observed data (observational data) for MB discovery. However, almost all existing MB discovery methods are designed for finding MBs from a single observational dataset. To identify MBs from multiple interventional datasets, we face two challenges: (1) unknown intervention variables; (2) nonidentical data distributions. To tackle the challenges, we theoretically analyze (a) under what conditions we can find the correct MB of a target variable, and (b) under what conditions we can identify the causes of the target variable via discovering its MB. Based on the theoretical analysis, we propose a new algorithm for discovering MBs from multiple interventional datasets, and present the conditions/assumptions which assure the correctness of the algorithm. To our knowledge, this work is the first to present the theoretical analyses about the conditions for MB discovery in multiple interventional datasets and the algorithm to find the MBs in relation to the conditions. Using benchmark Bayesian networks and real-world datasets, the experiments have validated the effectiveness and efficiency of the proposed algorithm in the paper.

Predictive models such as decision trees and neural networks may produce discrimination in their predictions. This paper proposes a method to post-process the predictions of a predictive model to make the processed predictions non-discriminatory. The method considers multiple protected variables together. Multiple protected variables make the problem more challenging than a simple protected variable. The method uses a well-cited discrimination metric and adapts it to allow the specification of explanatory variables, such as position, profession, education, that describe the contexts of the applications. It models the post-processing of predictions problem as a nonlinear optimization problem to find best adjustments to the predictions so that the discrimination constraints of all protected variables are all met at the same time. The proposed method is independent of classification methods. It can handle the cases that existing methods cannot handle: satisfying multiple protected attributes at the same time, allowing multiple explanatory attributes, and being independent of classification model types. An evaluation using four real world data sets shows that the proposed method is as effectively as existing methods, in addition to its extra power.

Algorithmic discrimination is an important aspect when data is used for predictive purposes. This paper analyzes the relationships between discrimination and classification, data set partitioning, and decision models, as well as correlation. The paper uses real world data sets to demonstrate the existence of discrimination and the independence between the discrimination of data sets and the discrimination of classification models., Comment: arXiv admin note: text overlap with arXiv:1811.01480

International audience; Book Front Matter of AICT 538

Causal discovery studies the problem of mining causal relationships between variables from data, which is of primary interest in science. During the past decades, significant amount of progresses have been made toward this fundamental data mining paradigm. Recent years, as the availability of abundant large-sized and complex observational data, the constrain-based approaches have gradually attracted a lot of interest and have been widely applied to many diverse real-world problems due to the fast running speed and easy generalizing to the problem of causal insufficiency. In this paper, we aim to review the constraint-based causal discovery algorithms. Firstly, we discuss the learning paradigm of the constraint-based approaches. Secondly and primarily, the state-of-the-art constraint-based casual inference algorithms are surveyed with the detailed analysis. Thirdly, several related open-source software packages and benchmark data repositories are briefly summarized. As a conclusion, some open problems in constraint-based causal discovery are outlined for future research., Comment: This paper has been withdrawn by the author due to further improvement

Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complexity, which hinders their applications in real world high dimensional datasets, e.g gene expression datasets. In this paper, we present an R package, ParallelPC, that includes the parallelised versions of these causal exploration algorithms. The parallelised algorithms help speed up the procedure of experimenting big datasets and reduce the memory used when running the algorithms. The package is not only suitable for super-computers or clusters, but also convenient for researchers using personal computers with multi core CPUs. Our experiment results on real world datasets show that using the parallelised algorithms it is now practical to explore causal relationships in high dimensional datasets with thousands of variables in a single multicore computer. ParallelPC is available in CRAN repository at https://cran.rproject.org/web/packages/ParallelPC/index.html.

Differential dependencies (DDs) capture the relationships between data columns of relations. They are more general than functional dependencies (FDs) and and the difference is that DDs are defined on the distances between values of two tuples, not directly on the values. Because of this difference, the algorithms for discovering FDs from data find only special DDs, not all DDs and therefore are not applicable to DD discovery. In this paper, we propose an algorithm to discover DDs from data following the way of fixing the left hand side of a candidate DD to determine the right hand side. We also show some properties of DDs and conduct a comprehensive analysis on how sampling affects the DDs discovered from data.

Nature methods 16(9), 843-852 (2019). doi:10.1038/s41592-019-0509-5, Published by Nature Publishing Group, London [u.a.]

Refereed/Peer-reviewed Directed acyclic graph (DAG) learning plays a key role in causal discovery and many machine learning tasks. Learning a DAG from high-dimensional data always faces scalability problems. A local-to-global DAG learning approach can be scaled to high-dimensional data, however, existing local-to-global DAG learning algorithms employ either the AND-rule or the OR-rule for constructing a DAG skeleton. Simply using either rule, existing local-to-global methods may learn an inaccurate DAG skeleton, leading to unsatisfactory DAG learning performance. To tackle this problem, in this paper, we propose an A daptive D AG L earning (ADL) algorithm. The novel contribution of ADL is that it can simultaneously and adaptively use the AND-rule and the OR-rule to construct an accurate global DAG skeleton. We conduct extensive experiments on both benchmark and real-world datasets, and the experimental results show that ADL is significantly better than some existing local-to-global and global DAG learning algorithms.

usc Causal feature selection methods aim to identify a Markov boundary (MB) of a class variable, and almost all the existing causal feature selection algorithms use conditional independence (CI) tests to learn the MB. However, in real-world applications, due to data issues (e.g., noisy or small samples), CI tests can be unreliable; thus, causal feature selection algorithms relying on CI tests encounter two types of errors: false positives (i.e., selecting false MB features) and false negatives (i.e., discarding true MB features). Existing algorithms only tackle either false positives or false negatives, and they cannot deal with both types of errors at the same time, leading to unsatisfactory results. To address this issue, we propose a dual-correction-strategy-based MB learning (DCMB) algorithm to correct the two types of errors simultaneously. Specifically, DCMB selectively removes false positives from the MB features currently selected, while selectively retrieving false negatives from the features currently discarded. To automatically determine the optimal number of selected features for the selective removal and retrieval in the dual correction strategy, we design the simulated-annealing-based DCMB (SA-DCMB) algorithm. Using benchmark Bayesian network (BN) datasets, the experimental results demonstrate that DCMB achieves substantial improvements on the MB learning accuracy compared with the existing MB learning methods. Empirical studies in real-world datasets validate the effectiveness of SA-DCMB for classification against state-of-the-art causal and traditional feature selection algorithms. Refereed/Peer-reviewed

Refereed/Peer-reviewed Many educational institutions are using predictive models to leverage actionable insights using student data and drive student success. A common task has been predicting students at risk of dropping out for the necessary interventions to be made. However, issues of discrimination by these predictive models based on protected attributes of students have recently been raised. An important question that is constantly asked is: should the protected attributes be excluded from the learning analytics (LA) models in order to ensure fairness? In this work, we aimed at answering questions that if we exclude the protected attributes from the LA models, does the exclusion ensure fairness as it should? Does the exclusion affect the performance of the LA model? If so, why? We found answers to these questions and went further to explain why. We built machine learning models and performed empirical evaluations using a 3-year dropout data for a particular program in a large Australian university. We found that excluding or including the protected attributes had marginal effect on predictive performance and fairness. Perhaps not surprisingly, our findings suggest that the effect of including or excluding protected attributes is a function of how they relate with the prediction outcome. More specifically, if a protected attribute is correlated with the target label and proves to be an important feature, then their inclusion or exclusion would have effect on the performance and fairness and vice versa. Our findings provide insightful information that can be used by relevant stakeholders to make well-informed decisions.

In personalised decision making, evidence is required to determine whether an action (treatment) is suitable for an individual. Such evidence can be obtained by modelling treatment effect heterogeneity in subgroups. The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual. In this paper, we design a Treatment effect pattern (TEP) to represent treatment effect heterogeneity in data. To achieve an interpretable presentation of TEPs, we use a local causal structure around the outcome to explicitly show how those important variables are used in modelling. We also derive a formula for unbiasedly estimating the Conditional Average Causal Effect (CATE) using the local structure in our problem setting. In the discovery process, we aim at minimising heterogeneity within each subgroup represented by a pattern. We propose a bottom-up search algorithm to discover the most specific patterns fitting individual circumstances the best for personalised decision making. Experiments show that the proposed method models treatment effect heterogeneity better than three other existing tree based methods in synthetic and real world data sets.

Refereed/Peer-reviewed Topic modelling is important for tackling several data mining tasks in information retrieval. While seminal topic modelling techniques such as Latent Dirichlet Allocation (LDA) have been proposed, the ubiquity of social media and the brevity of its texts pose unique challenges for such traditional topic modelling techniques. Several extensions including auxiliary aggregation, self aggregation and direct learning have been proposed to mitigate these challenges, however some still remain. These include a lack of consistency in the topics generated and the decline in model performance in applications involving disparate document lengths. There is a recent paradigm shift towards neural topic models, which are not suited for resource-constrained environments. This paper revisits LDA-style techniques, taking a theoretical approach to analyse the relationship between word co-occurrence and topic models. Our analysis shows that by altering the word co-occurrences within the corpus, topic discovery can be enhanced. Thus we propose a novel data transformation approach dubbed DATM to improve the topic discovery within a corpus. A rigorous empirical evaluation shows that DATM is not only powerful, but it can also be used in conjunction with existing benchmark techniques to significantly improve their effectiveness and their consistency by up to 2 fold.

The ultimate goal in adversarial defense is to build a universally robust defense against all types of attacks, but ongoing arms race between adversarial attacks and defenses show the difficulty in building a deterministic defense to work towards the goal. Leveraging the idea of a mixture model, in this paper, we introduce a new Randomized Adversarial Defense method (RAD) to increase the robustness against adversarial examples. RAD is designed as a simple, yet effective random mixture of a global model and one or more local models. The mixture is able to create a random decision boundary for a test instance, making it harder for an adversarial example to succeed, and thus increasing the robustness of the defense. The global model is adversarially trained to provide the baseline robustness. The local models are aimed to supplement the global model and thus the decision boundary of a local model is expected to be adjacent to the decision boundary of the global model. These models then together form a random mixture to create a randomized (non-deterministic) decision boundary for each test instance at the end. Such a randomization scheme reduces the adversarial risk since the adversary has to approximate the best attack despite of the given complete knowledge of the parameters of the individual classifiers. By proposing the notion of having a global and local models with different focuses in the mixture and the way of creating a local model which has minimum dependency on the base (global) model, RAD provides a simpler and more flexible, yet effective approach building a randomized defense, compared with the existing randomization based methods. Experimental results show that our simple randomization approach outperforms the most robust deterministic defense method and performs competitively upon the existing randomized defense method against strong adaptive attacks on CIFAR10 and CIFAR100. Refereed/Peer-reviewed

The COVID-19 pandemic forced many educational institutions to transition to online learning activities. This significantly impacted various aspects of students’ lives. Many of the studies aimed at assessing the impact of the online instruction on students’ wellbeing and performance have mainly focused on issues such as mental health. However, the impact on student grades—a key measure of student success—has been given little attention. The handful existing studies are either focused on primary schools—where the dynamics are different from higher education—or based on statistical correlations, which are usually not causally rigorous, therefore, prone to biased estimates due to various confounding variables. There are many variables associated with students’ grades, thus, to assess the causal impact of the online instruction on students’ grades, there is a need for a causally-grounded approach that can control for confounding variables. To that end, we use a causal tree to investigate the impact of online instruction on the grades of the general population as well as different demographic subgroups. Our analysis is based on the demographic and engagement data for the 2019 (offline/control) and 2020 (online/treatment) cohorts of 3 mandatory courses in an Australian university. For all 3 courses, our results show that for any given student in the population, the average grade they would have gotten, had they studied offline, reduced by 3.6%, 4.7%, and 14% respectively. Further analyses show that among students with similar level of (low) engagement with the virtual learning environment, the average grade international students would have gotten, had they studied face-to-face, reduced by 19.9%, 36.6%, and 46.9% more than their domestic counterparts despite having similar engagement for the 3 courses respectively. These subgroup disparities have the potential to exacerbate existing inequalities. Given the current concerns about algorithmic bias in learning analytics (LA), we trained grade prediction models with the data and investigated for algorithmic bias. Interestingly, we find that by simply changing citizenship status, a student gets a new predicted grade, entirely different from what was initially predicted given their actual citizenship status. This implies that researchers must be careful when building LA models on COVID-19 era data. Refereed/Peer-reviewed

Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when there exist latent confounders. With the standard IV method, when a given IV is valid, unbiased estimation can be obtained, but the validity requirement on a standard IV is strict and untestable. Conditional IVs have been proposed to relax the requirement of standard IVs by conditioning on a set of observed variables (known as a conditioning set for a conditional IV). However, the criterion for finding a conditioning set for a conditional IV needs a directed acyclic graph (DAG) representing the causal relationships of both observed and unobserved variables. This makes it challenging to discover a conditioning set directly from data. In this paper, by leveraging maximal ancestral graphs (MAGs) for causal inference with latent variables, we study the graphical properties of ancestral IVs, a type of conditional IVs using MAGs, and develop the theory to support data-driven discovery of the conditioning set for a given ancestral IV in data under the pretreatment variable assumption. Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data. Extensive experiments on synthetic and real-world datasets demonstrate the performance of the algorithm in comparison with existing IV methods Refereed/Peer-reviewed

The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of samples along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at https://github.com/AndresMCB/DynamicCancerDriver

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data Refereed/Peer-reviewed

Polygenic risk scores are emerging as a potentially powerful tool to predict future phenotypes of target individuals, typically using unrelated individuals, thereby devaluing information from relatives. Here, for 50 traits from the UK Biobank data, we show that a design of 5,000 individuals with first-degree relatives of target individuals can achieve a prediction accuracy similar to that of around 220,000 unrelated individuals (mean prediction accuracy = 0.26 vs. 0.24, mean fold-change = 1.06 (95% CI: 0.99-1.13), P-value = 0.08), despite a 44-fold difference in sample size. For lifestyle traits, the prediction accuracy with 5,000 individuals including first-degree relatives of target individuals is significantly higher than that with 220,000 unrelated individuals (mean prediction accuracy = 0.22 vs. 0.16, mean fold-change = 1.40 (1.17-1.62), P-value = 0.025). Our findings suggest that polygenic prediction integrating family information may help to accelerate precision health and clinical intervention., Genetic data from large cohorts of unrelated individuals can be used to create polygenic risk scores, which could be used to predict individual risk of developing a specific disease. Here the authors show that smaller cohorts of related individuals can provide similarly powerful predictive ability.

Refereed/Peer-reviewed A central question in many fields of scientific research is to determine how an outcome is affected by an action, i.e., to estimate the causal effect or treatment effect of an action. In recent years, in areas such as personalised healthcare, sociology, and online marketing, a need has emerged to estimate heterogeneous treatment effects with respect to individuals of different characteristics. To meet this need, two major approaches have been taken: treatment effect heterogeneity modelling and uplifting modelling. Researchers and practitioners in different communities have developed algorithms based on these approaches to estimate the heterogeneous treatment effects. In this article, we present a unified view of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We provide a structured survey of existing methods following either of the two approaches, emphasising their inherent connections and using unified notation to facilitate comparisons. We also review the main applications of the surveyed methods in personalised marketing, personalised medicine, and sociology. Finally, we summarise and discuss the available software packages and source codes in terms of their coverage of different methods and applicability to different datasets, and we provide general guidelines for method selection.

In Disability Employment Services (DES), an emerging problem is recommending to disabled workers the right skill to upgrade and the right upgrade level to achieve a maximum increase in their job retention time. This problem involves causal reasoning to estimate the individual causal effect (ICE) on the survival outcome, i.e., job retention time, to determine the most effective intervention for a worker. Existing methods are not suitable to solve our problem. They are mostly developed for non-causal or non-survival challenges, while methods for causal survival analysis are under-explored. This paper proposes a representation learning method for recommending personalized interventions that can generate a maximum increase in job retention time for workers with disability. In our method, observed covariates are disentangled into latent variables based on which confounding and censoring biases are eliminated, and the ICE prediction model is built. Since true ICE values are not directly measurable in observational data, a reverse engineering technique is developed to estimate ICE for training samples. These estimated ICE values are then used as the pseudo ground truth to train the prediction model. Experiments with a case study of Australian workers with disability show that by adopting personalized interventions recommended by our method, disabled workers can increase their job retention time by up to 2.8 months. Additional evaluations with public datasets also show the technical strengths of our method in other applications. Refereed/Peer-reviewed

Refereed/Peer-reviewed An emerging problem in Disability Employment Services(DES) is recommending to disabled jobseekers the right skill to up grade and the right upgrade level to achieve maximum increase in their employment potential. This problem involves causal reasoning to estimate the causal effect on employment status to determine the most effective personalized intervention. In this paper, we propose a causal graph based method to solve the intervention recommendation problem. Personalized causal graphs of individual training samples are reverse engineered from a population-level causal graph using linear interpolation. A prediction model is built from these personalized graphs to recommend interventions. Experiments with a case study from an Australian DES providers how that by adopting interventions recommended by our method, disabled jobseekers would increase their employability by up to 24%. Evaluations with public datasets also show its advantages in other applications