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Social media listening has been increasingly acknowledged as a tool with applications in many stages of the drug development process. These applications were created to meet the need for patient-centric therapies that are fit-for-purpose and meaningful to patients. Such applications, however, require the leverage of new quantitative approaches and analytical methods that draw from developments in artificial intelligence and real-world data (RWD) analysis. Here, we review the state-of-the-art in quantitative social media listening (QSML) methods applied to drug discovery from the perspective of the pharmaceutical industry.

A key step in clinical flow cytometry data analysis is gating, which involves the identification of cell populations. The process of gating produces a set of reportable results, which are typically described by gating definitions. The non-standardized, non-interpreted nature of gating definitions represents a hurdle for data interpretation and data sharing across and within organizations. Interpreting and standardizing gating definitions for subsequent analysis of gating results requires a curation effort from experts. Machine learning approaches have the potential to help in this process by predicting expert annotations associated with gating definitions.We created a gold-standard dataset by manually annotating thousands of gating definitions with cell type and functional marker annotations. We used this dataset to train and test a machine learning pipeline able to predict standard cell types and functional marker genes associated with gating definitions.The machine learning pipeline predicted annotations with high accuracy for both cell types and functional marker genes. Accuracy was lower for gating definitions from assays belonging to laboratories from which limited or no prior data was available in the training. Manual error review ensured that resulting predicted annotations could be reused subsequently as additional gold-standard training data.Machine learning methods are able to consistently predict annotations associated with gating definitions from flow cytometry assays. However, a hybrid automatic and manual annotation workflow would be recommended to achieve optimal results.

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user’s publicly available tweets (the user’s ‘timeline’). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user’s timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user’s timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore ‘Challenges in Mining Drug Adverse Reactions’. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations. M.K.: This work was supported in part through the collaboration between the Spanish Plan for the Advancement of Language Technology (Plan TL) and the Barcelona Supercomputing Center; we also acknowledge the 2020 Proyectos de I+D+i - RTI Tipo A (PID2020-119266RA-I00) for support. Ö.U.: This study was supported in part by the National Library of Medicine under Award Number R15LM013209 and R13LM013127.

Email is the primary means of communication for scientists. However, scientific authors change email address over time. Using a new method, we have calculated that approximately 18% of all authors’ contact email addresses in MEDLINE are invalid. While an unfortunate number, it is, however, lower than previously estimated. To mitigate this problem, institutions should provide email forwarding and scientific authors should use more stable email addresses. In fact, a steadily growing share already use free private email addresses: 32% of all new addresses in MEDLINE in 2018 were of this kind.

Current biological writing is afflicted by the use of ambiguous names, convoluted sentences, vague statements and narrative-fitted storylines. This represents a challenge for biological research in general and in particular for fields such as biological database curation and text mining, which have been tasked to cope with exponentially growing content. Improving the quality of biological writing by encouraging unambiguity and precision would foster expository discipline and machine reasoning. More specifically, the routine inclusion of formal languages in biological writing would improve our ability to describe, compile and model biology.

BackgroundIt is estimated that there are more than 7,000 rare diseases (RDs) worldwide, impacting the lives of approximately 400 million people and only 5% have an approved therapy. Facing special challenges, including patient scarceness, incomplete knowledge of the natural history and only few specialized clinical sites, clinical trials (CT) are limited, making the data from trials critical for research and clinical care. Despite the introduction of the U.S. Food and Drug Administration Amendment Act (FDAAA) in 2007 requiring certain CTs to post results on the registry ClinicalTrials.gov within 12 months following completion, compliance has been reportedly poor. Here, we describe general characteristics of RD CTs, identify trends, and evaluate result reporting practices under the FDAAA aiming to draw awareness to the problem of non-compliance.MethodsCTs conducted between 2008 and 2015 were extracted from the public U.S. trial registry ClinicalTrials.gov using the text mining software I2E (Linguamatics). Disease names were matched with rare disease names from the Orphanet Rare Disease Ontology (ORDO, v2.5, Orphanet). Statistical analyses and data visualization were performed using GraphPad Prism 7 and R (v3.5). The Student’s t-test was employed to calculate significance using p-value cut-offs of ResultsWe analyzed 1,056 RD CTs of which 55.7% were phase 2, 7.7% phase 2/3 and 36.7% phase 3 trials. The studies were mostly one- and two-armed experimental CTs with the majority (60.2%) being funded by industry. Cystic fibrosis and sickle cell disease represented the most frequently investigated diseases (25.0% and 16.5%). Industry-led phase 2 RD CTs were significantly (pClinicalTrial.gov.ConclusionRD CTs tend to be comparatively small, industry-funded studies focusing on genetic and neurologic conditions. Sponsor-related differences in study design, duration, and enrollment were observed. There are still substantial shortcomings when it comes to result publication.

Background: Individuals with Parkinson’s disease (PD) develop a significant disease burden over time that contributes to a progressive decline in health-related quality of life. There is a paucity of qualitative research to understand symptoms and impacts in individuals with early-stage PD (i.e., Hoehn and Yahr stage 1–2 and ≤2 years since diagnosis). Objective: The collection of qualitative data to inform the selection of clinical outcome assessments for clinical trials is advocated by regulators. This patient-centered, multistage study sought to create a conceptual model of symptoms and their impact for individuals with early-stage PD. Methods: Symptoms and impacts of PD were gathered from a literature review of qualitative research, a quantitative social media listening analysis, and qualitative patient concept elicitation interviews (n = 35). Clinical experts provided input to validate and finalize the concepts. Results: The final conceptual model consisted of 27 symptoms categorized into ‘motor’ or ‘non-motor’ domains, and 39 impacts divided into five domains. Most frequently reported symptoms in early-stage PD were ‘tremors’ (89%), ‘stiffness and rigidity’, and ‘fatigue’ (69%, both). Most frequently reported impacts included ‘anxiety’ (74%), ‘eating and drinking’ (71%), followed by ‘exercise/sport’ and ‘relationship with family/family life’ (66%, both). Conclusion: This study provides initial insights relating to the symptom and impact burden of early-stage PD patients. The conceptual model can be used to help researchers to develop and select optimal patient-centered outcomes to measure treatment benefit in clinical trials. These findings could inform future qualitative research and the development of outcomes specifically for early-stage PD patients.

BackgroundA key step in clinical flow cytometry data analysis is gating, which involves the identification of cell populations. The process of gating produces a set of reportable results, which are typically described by gating definitions. The non-standardized, non-interpreted nature of gating definitions represents a hurdle for data interpretation and data sharing across and within organizations. Interpreting and standardizing gating definitions for subsequent analysis of gating results requires a curation effort from experts. Machine learning approaches have the potential to help in this process by predicting expert annotations associated with gating definitions.MethodsWe created a gold-standard dataset by manually annotating thousands of gating definitions with cell type and functional marker annotations. We used this dataset to train and test a machine learning pipeline able to predict standard cell types and functional marker genes associated with gating definitions.ResultsThe machine learning pipeline predicted annotations with high accuracy for both cell types and functional marker genes. Accuracy was lower for gating definitions from assays belonging to laboratories from which limited or no prior data was available in the training. Manual error review ensured that resulting predicted annotations could be reused subsequently as additional gold-standard training data.ConclusionsMachine learning methods are able to consistently predict annotations associated with gating definitions from flow cytometry assays. However, a hybrid automatic and manual annotation workflow would be recommended to achieve optimal results.

Information silos have been an oft-maligned feature of scientific research for introducing a bias towards knowledge that is produced within a scientist’s own community. The vastness of the scientific literature has been commonly blamed for this phenomenon, despite recent improvements in information retrieval and text mining. Its actual negative impact on scientific progress, however, has never been quantified. This analysis attempts to do so by exploring its effects on biomedical discovery, particularly in the discovery of relations between diseases, genes and chemical compounds. Results indicate that the probability that two scientific facts will enable the discovery of a new fact depends on how far apart these two facts were published within the scientific landscape. In particular, the probability decreases exponentially with the citation distance. Thus, the direction of scientific progress is distorted based on the location in which each scientific fact is published, representing a path-dependent bias in which originally closely-located discoveries drive the sequence of future discoveries. To counter this bias, scientists should open the scope of their scientific work with modern computational approaches.

Background Numerous efforts have been poured into annotating the wealth of knowledge contained in biomedical articles. Thanks to such efforts, it is now possible to quantitatively explore relations between these annotations and the citation network at large scale. Results With the aid of several large and small annotation databases, this study shows that articles share annotations with their citation neighborhood to the point that the neighborhood’s most common annotations are likely to be those appearing in the article. Conclusions These findings posit that an article’s citation neighborhood defines to a large extent the article’s annotated content. Thus, citations should be considered as a foundation for future knowledge management and annotation of biomedical articles.

BACKGROUND The abundance of online content contributed by patients is a rich source of insight about the lived experience of disease. Patients share disease experiences with other members of the patient and caregiver community and do so using their own lexicon of words and phrases. This lexicon and the topics that are communicated using words and phrases belonging to the lexicon help us better understand disease burden. Insights from social media may ultimately guide clinical development in ways that ensure that future treatments are fit for purpose from the patient’s perspective. OBJECTIVE We sought insights into the patient experience of chronic obstructive pulmonary disease (COPD) by analyzing a substantial corpus of social media content. The corpus was sufficiently large to make manual review and manual coding all but impossible to perform in a consistent and systematic fashion. Advanced analytics were applied to the corpus content in the search for associations between symptoms and impacts across the entire text corpus. METHODS We conducted a retrospective, cross-sectional study of 5663 posts sourced from open blogs and online forum posts published by COPD patients between February 2016 and August 2019. We applied a novel neural network approach to identify a lexicon of community words and phrases used by patients to describe their symptoms. We used this lexicon to explore the relationship between COPD symptoms and disease-related impacts. RESULTS We identified a diverse lexicon of community words and phrases for COPD symptoms, including gasping, wheezy, mucus-y, and muck. These symptoms were mentioned in association with specific words and phrases for disease impact such as frightening, breathing discomfort, and difficulty exercising. Furthermore, we found an association between mucus hypersecretion and moderate disease severity, which distinguished mucus from the other main COPD symptoms, namely breathlessness and cough. CONCLUSIONS We demonstrated the potential of neural networks and advanced analytics to gain patient-focused insights about how each distinct COPD symptom contributes to the burden of chronic and acute respiratory illness. Using a neural network approach, we identified words and phrases for COPD symptoms that were specific to the patient community. Identifying patterns in the association between symptoms and impacts deepened our understanding of the patient experience of COPD. This approach can be readily applied to other disease areas.

Background The abundance of online content contributed by patients is a rich source of insight about the lived experience of disease. Patients share disease experiences with other members of the patient and caregiver community and do so using their own lexicon of words and phrases. This lexicon and the topics that are communicated using words and phrases belonging to the lexicon help us better understand disease burden. Insights from social media may ultimately guide clinical development in ways that ensure that future treatments are fit for purpose from the patient’s perspective. Objective We sought insights into the patient experience of chronic obstructive pulmonary disease (COPD) by analyzing a substantial corpus of social media content. The corpus was sufficiently large to make manual review and manual coding all but impossible to perform in a consistent and systematic fashion. Advanced analytics were applied to the corpus content in the search for associations between symptoms and impacts across the entire text corpus. Methods We conducted a retrospective, cross-sectional study of 5663 posts sourced from open blogs and online forum posts published by COPD patients between February 2016 and August 2019. We applied a novel neural network approach to identify a lexicon of community words and phrases used by patients to describe their symptoms. We used this lexicon to explore the relationship between COPD symptoms and disease-related impacts. Results We identified a diverse lexicon of community words and phrases for COPD symptoms, including gasping, wheezy, mucus-y, and muck. These symptoms were mentioned in association with specific words and phrases for disease impact such as frightening, breathing discomfort, and difficulty exercising. Furthermore, we found an association between mucus hypersecretion and moderate disease severity, which distinguished mucus from the other main COPD symptoms, namely breathlessness and cough. Conclusions We demonstrated the potential of neural networks and advanced analytics to gain patient-focused insights about how each distinct COPD symptom contributes to the burden of chronic and acute respiratory illness. Using a neural network approach, we identified words and phrases for COPD symptoms that were specific to the patient community. Identifying patterns in the association between symptoms and impacts deepened our understanding of the patient experience of COPD. This approach can be readily applied to other disease areas.

Motivation Name ambiguity has long been a central problem in biomedical text mining. To tackle it, it has been usually assumed that names present only one meaning within a given text. It is not known whether this assumption applies beyond the scope of single documents. Results Using a new method that leverages large numbers of biomedical annotations and normalized citations, this study shows that ambiguous biomedical names mentioned in scientific articles tend to present the same meaning in articles that cite them or that they cite, and, to a lesser extent, two steps away in the citation network. Citations, therefore, can be regarded as semantic connections between articles and the citation network should be considered for tasks such as automatic name disambiguation, entity linking and biomedical database annotation. A simple experiment shows the applicability of these findings to name disambiguation. Availability and implementation The code used for this analysis is available at: https://github.com/raroes/one-sense-per-citation-network.

Email is the primary means of communication for scientists. However, scientific authors change email address over time. Using a new method, we have calculated that approximately 18% of all authors’ contact email addresses in MEDLINE are invalid. While an unfortunate number, it is, however, lower than previously estimated. To mitigate this problem, institutions should provide email forwarding and scientific authors should use more stable email addresses. In fact, a steadily growing share already use free private email addresses: 32% of all new addresses in MEDLINE in 2018 were of this kind.

Background We have previously described evidence for a statistically significant, global, supra-chromosomal representation of the human body that appears to stretch over the entire genome. Results Here, we extend the genome mapping model, zooming down to the typical individual animal cell. Its cellular organization appears to be significantly mapped onto the human genome: Evidence is reported for a “cellunculus” — on the model of a homunculus, on the H. sapiens genome. Conclusions Basic cell structure turns out to map similarly onto the total genome, mirrored via genes that express in particular cell organelles (e.g., “nuclear membrane”). Similar cell maps may also appear on individual chromosomes that map topologically on the dorsoventral body axis. This seems to constitute some of the basic structural and functional organization of nucleus and chromosome architecture. Electronic supplementary material The online version of this article (10.1186/s13039-019-0426-4) contains supplementary material, which is available to authorized users.

Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery.

Objective Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorithms, however, require adequately designed gold standards that reflect the reference database properly. In this study we used MEDLINE to build the first unbiased gold standard in a reference database and improve over the existing state of the art in author disambiguation. Materials and Methods Following a new corpus design method, publication pairs randomly picked from MEDLINE were evaluated by both crowdsourcing and expert curators. Because the latter showed higher accuracy than crowdsourcing, expert curators were tasked to create a full corpus. The corpus was then used to explore new features that could improve state-of-the-art author disambiguation algorithms that would not have been discoverable with previously existing gold standards. Results We created a gold standard based on 1900 publication pairs that shows close similarity to MEDLINE in terms of chronological distribution and information completeness. A machine-learning algorithm that includes new features related to the ethnic origin of authors showed significant improvements over the current state of the art and demonstrates the necessity of realistic gold standards to further develop effective author disambiguation algorithms. Discussion and Conclusion An unbiased gold standard can give a more accurate picture of the status of author disambiguation research and help in the discovery of new features for machine learning. The principles and methods shown here can be applied to other reference databases beyond MEDLINE. The gold standard and code used for this study are available at the following repository: https://github.com/amorgani/AND/

Background Differential gene expression is important to understand the biological differences between healthy and diseased states. Two common sources of differential gene expression data are microarray studies and the biomedical literature. Methods With the aid of text mining and gene expression analysis we have examined the comparative properties of these two sources of differential gene expression data. Results The literature shows a preference for reporting genes associated to higher fold changes in microarray data, rather than genes that are simply significantly differentially expressed. Thus, the resemblance between the literature and microarray data increases when the fold-change threshold for microarray data is increased. Moreover, the literature has a reporting preference for differentially expressed genes that (1) are overexpressed rather than underexpressed; (2) are overexpressed in multiple diseases; and (3) are popular in the biomedical literature at large. Additionally, the degree to which diseases are similar depends on whether microarray data or the literature is used to compare them. Finally, vaguely-qualified reports of differential expression magnitudes in the literature have only small correlation with microarray fold-change data. Conclusions Reporting biases of differential gene expression in the literature can be affecting our appreciation of disease biology and of the degree of similarity that actually exists between different diseases. Electronic supplementary material The online version of this article (10.1186/s12920-017-0293-y) contains supplementary material, which is available to authorized users.

Fully automated text mining (TM) systems promote efficient literature searching,\ud retrieval, and review but are not sufficient to produce ready-to-consume curated documents.\ud These systems are not meant to replace biocurators, but instead to assist them in\ud one or more literature curation steps. To do so, the user interface is an important aspect\ud that needs to be considered for tool adoption. The BioCreative Interactive task (IAT) is a\ud track designed for exploring user-system interactions, promoting development of useful\ud TM tools, and providing a communication channel between the biocuration and the TM\ud communities. In BioCreative V, the IAT track followed a format similar to previous interactive\ud tracks, where the utility and usability of TM tools, as well as the generation of use\ud cases, have been the focal points. The proposed curation tasks are user-centric and\ud formally evaluated by biocurators. In BioCreative V IAT, seven TM systems and 43 biocurators\ud participated. Two levels of user participation were offered to broaden curator involvement\ud and obtain more feedback on usability aspects. The full level participation\ud involved training on the system, curation of a set of documents with and without TM\ud assistance, tracking of time-on-task, and completion of a user survey. The partial level\ud participation was designed to focus on usability aspects of the interface and not the performance\ud per se. In this case, biocurators navigated the system by performing predesigned\ud tasks and then were asked whether they were able to achieve the task and the\ud level of difficulty in completing the task. In this manuscript, we describe the development of the interactive task, from planning to execution and discuss major findings for the systems tested.

The approach of drug repositioning is an important consideration for any life science organization. By using knowledge-driven systems in the form of large data stores and applying rational in silico experimental design, researchers have generated workflows that are capable of identifying novel uses for drugs that span the therapeutic pipeline and beyond. Both broadly accessible data, such as Medline and Chembank, in addition to internal proprietary data of the company in the form of gene chip experiments, compound screening databases, and clinical trial information play an important role in the success of drug repositioning. By reviewing how current and past successes have been accomplished along with the data used, important stratagems emerge that can provide a wealth of ideas for novel workflows, as well as provide a guide for future discoveries.

Motivation: Figures from biomedical articles contain valuable information difficult to reach without specialized tools. Currently, there is no search engine that can retrieve specific figure types. Results: This study describes a retrieval method that takes advantage of principles in image understanding, text mining and optical character recognition (OCR) to retrieve figure types defined conceptually. A search engine was developed to retrieve tables and figure types to aid computational and experimental research. Availability: http://iossifovlab.cshl.edu/figurome Contact: raul.rodriguez-esteban@pfizer.com

We describe a new tool for visualization of biomedical scientific trends. The method captures variations in scientific impact over time to allow for a comparison of relative significance and evolution of fields similar to a financial market scorecard. The tool is available at SciTrends (http://www.scitrends.net), depicting the evolution of almost 200 thousand biomedical fields in time. With millions of articles on thousands of topics published in biomedicine, we envision that only with such large-scale tools researchers can objectively understand the ever-changing interests in the biomedical sciences and make more informed decisions.

SummaryOne drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound (“drug”) pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate.

Motivation: A crucial phenomenon of our times is the diminishing marginal returns of investments in pharmaceutical research and development. A potential reason is that research into diseases is becoming increasingly complex, and thus more burdensome, for humans to handle. We sought to investigate whether we could measure research complexity by analyzing the published literature. Results: Through the text mining of the publication record of multiple diseases, we have found that the complexity and novelty of disease research has been increasing over the years. Surprisingly, we have also found that research on diseases with higher publication rate does not possess greater complexity or novelty than that on less-studied diseases. We have also shown that the research produced about a disease can be seen as a differentiated area of knowledge within the wider biomedical research. For our analysis, we have conceptualized disease research as a parallel multi-agent search in which each scientific agent (a scientist) follows a search path based on a model of a disease. We have looked at trends in facts published for diseases, measured their diversity and turnover using the entropy measure and found similar patterns across disease areas. Contact: raul.rodriguez-esteban@roche.com

We have generated and made publicly available two very large networks of molecular interactions: 49,493 mouse-specific and 52,518 human-specific interactions. These networks were generated through automated analysis of 368,331 full-text research articles and 8,039,972 article abstracts from the PubMed database, using the GeneWays system. Our networks cover a wide spectrum of molecular interactions, such as bind, phosphorylate, glycosylate, and activate; 207 of these interaction types occur more than 1,000 times in our unfiltered, multi-species data set. Because mouse and human genes are linked through an orthological relationship, human and mouse networks are amenable to straightforward, joint computational analysis. Using our newly generated networks and known associations between mouse genes and cerebellar malformation phenotypes, we predicted a number of new associations between genes and five cerebellar phenotypes (small cerebellum, absent cerebellum, cerebellar degeneration, abnormal foliation, and abnormal vermis). Using a battery of statistical tests, we showed that genes that are associated with cerebellar phenotypes tend to form compact network clusters. Further, we observed that cerebellar malformation phenotypes tend to be associated with highly connected genes. This tendency was stronger for developmental phenotypes and weaker for cerebellar degeneration., Author Summary We described and made publicly available the largest existing set of text-mined statements; we also presented its application to an important biological problem. We have extracted and purified two large molecular networks, one for humans and one for mouse. We characterized the data sets, described the methods we used to generate them, and presented a novel biological application of the networks to study the etiology of five cerebellum phenotypes. We demonstrated quantitatively that the development-related malformations differ in their system-level properties from degeneration-related genes. We showed that there is a high degree of overlap among the genes implicated in the developmental malformations, that these genes have a strong tendency to be highly connected within the molecular network, and that they also tend to be clustered together, forming a compact molecular network neighborhood. In contrast, the genes involved in malformations due to degeneration do not have a high degree of connectivity, are not strongly clustered in the network, and do not overlap significantly with the development related genes. In addition, taking into account the above-mentioned system-level properties and the gene-specific network interactions, we made highly confident predictions about novel genes that are likely also involved in the etiology of the analyzed phenotypes.

A pattern of widespread connection optimization in the nervous system has become evident: deployment of some neural interconnections attains optimality, sometimes without detectable limits. New results for optimization of layout of connected areas of rat olfactory cortex and of rat amygdala are reported here. One larger question concerns mechanisms—how such minimization is attained. A next question is why a nervous system would optimize rather than just moderately satisfice. A morphogenic proposal that relates these questions is that the means of organizing neural wiring happens also to yield optimization. Some neuroanatomy is generated via “saving wire,” and this optimizing is via simple physical processes rather than DNA-mediated mechanisms. Such “non-genomic nativism” is thereby a path around fundamental limitations on generating brains, some of the most complex structures in the known universe.

Molecular perturbations provide a powerful toolset for biomedical researchers to scrutinize the contributions of individual molecules in biological systems. Perturbations qualify the context of experimental results and, despite their diversity, share properties in different dimensions in ways that can be formalized. We propose a formal framework to describe and classify perturbations that allows accumulation of knowledge in order to inform the process of biomedical scientific experimentation and target analysis. We apply this framework to develop a novel algorithm for automatic detection and characterization of perturbations in text and show its relevance in the study of gene-phenotype associations and protein-protein interactions in diabetes and cancer. Analyzing perturbations introduces a novel view of the multivariate landscape of biological systems.

In our recent article in EMBO reports (Cokol et al , 2007), we described a mathematical model and large‐scale data analysis to investigate the hidden factors underlying article retraction in scientific journals. Our study showed that retracted articles tend to appear more frequently in journals with a higher impact factor (IF). We proposed two possible explanations: either high‐IF journals publish flawed manuscripts at a higher rate, or articles appearing in these journals are subjected to a higher level of post‐publication scrutiny. As the latter provided a better explanation of our model and the data at our disposal, we concluded that low‐ and high‐IF journals publish flawed papers at similar rates. In his commentary on our study (Liu, 2007), Shi V. Liu makes various assertions that we would like to address. First, that we assume “…low‐IF journals probably receive less attention and are therefore likely to contain more articles that ought to be retracted, but instead go unnoticed.” He has mistaken our conclusion for an assumption. We used our model and analysis to conclude that low‐IF journals contain more hidden retractable papers than high‐IF journals. …

The Oreja software package (available from www.liv.ac.uk/psychology/Downloads/Oreja.htm) was designed to study speech intelligibility. It is a tool that allows manipulation of speech signals to facilitate study of human speech perception. A feature of this package is that it uses a high-level interpreted scripting environment (MATLAB), allowing the user to load, break down into different channels, analyze, and select the parts of the signal(s) of interest (e.g., attenuation of the amplitude of the selected channels, addition of noise, etc.).

Combinatorial network optimization theory concerns minimization of connection costs among interconnected components in systems such as electronic circuits. Similar wiring minimization can be observed at various levels of nervous systems, invertebrate and vertebrate, from placement of the entire brain in the body down to the subcellular level of neuron arbor geometry. In some cases, the minimization appears to be perfect or as good as can be detected with current methods. One question such “best of all possible brains” results raises is, what is the function of optimizing, rather than just reducing, these neural connection costs?

Biological curation, or biocuration, is often studied from the perspective of creating and maintaining databases that have the goal of mapping and tracking certain areas of biology. However, much biocuration is, in fact, dedicated to finite and time-limited projects in which insufficient resources demand trade-offs. This typically more ephemeral type of curation is nonetheless of importance in biomedical research. Here, I propose a framework to understand such restricted curation projects from the point of view of return on curation (ROC), value, efficiency and productivity. Moreover, I suggest general strategies to optimize these curation efforts, such as the 'multiple strategies' approach, as well as a metric called overhead that can be used in the context of managing curation resources.