Search

Protein-protein interactions play a fundamental role in drug design, gene therapy and vaccine development. The study of protein-protein interactions relies heavily on complex and time-consuming experiments, which has a severe impact on research throughputs. Thus, it is important to provide the experimentalist with the most promising cases by screening rapidly through a very large number of potential candidates. We propose a new deep neural network architecture that allows the binding probability for two proteins to be predicted instantly based solely on their amino acid sequences. Subsequently, screenings are performed based on the binding probabilities. The novelty of our approach lies in the fact that we consider self-binding and folding amino acid sequences, rather than just looking at these sequences per se. Our novel Siamese Pyramid Network (SPNet) architecture is inspired by Feature Pyramid Networks and consists of a multi-level Siamese neural network with an attention mechanism and a multilevel, trainable binding probability prediction network. Our experimental evaluation is performed on a strict dataset and shows that SPNet outperforms the state-of-the-art architectures. In addition, we employ SPNet to find the proteins that are most likely to bind with the Covid-2019 spike, thus providing a small and potentially valuable set of candidates for a future therapeutic vaccine., 2021 International Joint Conference on Neural Networks (IJCNN), July 18-22, 2021, Shenzhen, China

Glycosylation of hydrophobic peptides at one terminus effectively increases their water-solubility, and conjugation through the opposing end to a carrier protein, renders them more immunogenic. Moreover, the glycosylation minimizes antibody responses to potentially deleterious, non-productive terminal neo-epitope regions of the peptides, and consequently shifts peptide immunogenicity towards the core amino acid residues. As proof of concept, glycopeptide-protein conjugates related to influenza hemagglutinin (HA), neuraminidase (NA), and the dimerization loop region of human epidermal growth factor receptor 2 (Her2), demonstrated a favorable production of core peptide specific antibodies as determined by ELISA studies. Furthermore, glycosylated Her2 peptide conjugate antisera were also shown to recognize full length Her2 protein by ELISA and at the cell surface through flow cytometry analysis. In contrast, unmasked peptide conjugates generated significant antibody populations that were specific to the terminal neo-epitope of the peptide immunogen that are notably absent in parental proteins. Antibodies generated in this manner to peptides in the dimerization loop of Her2 are also functional as demonstrated by the growth inhibition of Her2 expressing SKBR3 carcinoma cells. This method provides a technique to tailor-make epitope-specific antibodies that may facilitate vaccine, therapeutic and diagnostic antibody development.

We address the problem of 3D protein deformable shape classification. Proteins are macromolecules characterized by deformable and complex shapes which are related to their function making their classification an important task. Their molecular surface is represented by graphs such as triangular tessellations or meshes. In this paper, we propose a new graph embedding based approach for the classification of these 3D deformable objects. Our technique is based on graphs decomposition into a set of substructures, using triangle-stars, which are subsequently matched with the Hungarian algorithm. The proposed approach is based on an approximation of the Graph Edit Distance which is characterized by its robustness against both noise and distortion. Our algorithm defines a metric space using graph embedding techniques, where each object is represented by a set of selected 3D prototypes. We propose new approaches for prototypes selection and features reduction. The classification is performed with supervised machine learning techniques. The proposed method is evaluated against 3D protein benchmark repositories and state-of-the-art algorithms. Our experimental results consistently demonstrate the effectiveness of our approach., 4th International Conference on Imaging, Vision & Pattern Recognition (IVPR-2020), June 22-25, 2020, Kitakyushu, Japan

In e-business, recommender systems have been instrumental in guiding users through their online experiences. However, these systems are often limited by the lack of labels data and data sparsity. Increasingly, data-mining techniques are utilized to address this issue. In most research, recommendations to be made are achieved via supervised learning that typically employs the k-nearest neighbor learner. However, supervised learning relies on labeled data, which may not be available at the time of learning. Data sparsity, which refers to situations where the number of items that have been recommended represents only a small subset of all available items, further affects model performance. One suggested solution is to apply cluster analysis as a preprocessing step and thus guide the learning process from natural grouping, typically using similar customer profiles, to improve predictive accuracy. In this paper, we study the benefits of applying cluster analysis as a preprocessing step prior to constructing classification models. Our HCC-Learn framework combines content-based analysis in the preprocessing stage and collaborative filtering in the final prediction stage. Our results show the value of our HCC-Learn framework applied to real-world data sets, especially when combining soft clustering and ensembles based on feature subspaces., 10th International Joint Conference, IC3K 2018, September 18-20, 2018, Seville, Spain, Series: Communications in Computer and Information Science

11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), Sept. 17-19, 2019, Vienna, Austria, Series: Communications in Computer and Information Science

CK2 is an essential protein kinase implicated in various cellular processes. In this study, we address a potential role of this kinase in chromatin modulations associated with transcription. We found that CK2 depletion from yeast cells leads to replication-independent increase of histone H3K56 acetylation and global activation of H3 turnover in coding regions. This suggests a positive role of CK2 in maintenance/recycling of the histone H3/H4 tetramers during transcription. Interestingly, strand-specific RNA-seq analyses show that CK2 inhibits global cryptic promoters driving both sense and antisense transcription. This further indicates a role of CK2 in the modulation of chromatin during transcription. Next, we showed that CK2 interacts with the major histone chaperone Spt6, and phosphorylates it in vivo and in vitro. CK2 phosphorylation of Spt6 is required for its cellular levels, for the suppression of histone H3 turnover and for the inhibition of spurious transcription. Finally, we showed that CK2 and Spt6 phosphorylation sites are important to various transcriptional responses suggesting that cryptic intragenic and antisense transcript production are associated with a defective adaptation to environmental cues. Altogether, our data indicate that CK2 mediated phosphorylation of Spt6 regulates chromatin dynamics associated with transcription, and prevents aberrant transcription.

The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the $\mbox{Tornado}$ framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the $\mbox{FHDDMS}$ and $\mbox{FHDDMS}_{add}$ approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our $\mbox{FHDDMS}$ variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art., 42 pages, and 14 figures

Pathologic complete response (pCR) is a predictor for favorable outcome after neoadjuvant treatment in early breast cancer. Modulation of gene expression may also provide early readouts of biological activity and prognosis, offering the possibility for timely response-guided treatment adjustment. The role of early transcriptional changes in predicting response to neoadjuvant chemotherapy plus bevacizumab was investigated. One-hundred-and-fifty patients with large, operable and locally advanced HER2-negative breast cancer received epirubicin and docetaxel, with the addition of bevacizumab. Patients underwent tumor biopsies at baseline, after Cycle 2 and at the time of surgery. The primary end point, pCR, and its relation with the secondary endpoints event-free survival (EFS), overall survival (OS) and gene expression profiles, are reported. The pCR rate was 13% (95% CI 8.6-20.2), with significantly more pCRs among triple-negative [28% (95% CI 14.8-45.4)] than among hormone receptor positive (HR+) tumors [9% (95% CI 4.6-16.3); (OR=3.9 [CI=1.5-10.3])]. pCR rates were not associated with EFS or OS. PAM50 subtypes significantly changed after Cycle 2 (p=0.03) and an index of absolute changes in PAM50 correlations between these time-points was associated with EFS [HR=0.62 (CI=0.3-1.1)]. In univariable analyses, signatures for angiogenesis, proliferation, estrogen receptor signaling, invasion and metastasis, and immune response, measured after Cycle 2, were associated with pCR in HR+ tumors. Evaluation of changes in molecular subtypes and other signatures early in the course of neoadjuvant treatment may be predictive of pCR and EFS. These factors may help guide further treatment and should be considered when designing neoadjuvant trials.

We propose a novel fast graph matching approach based on a new formulation of the stable marriage problem, to measure the distance between graphs. The proposed approach is optimal in terms of execution time, i.e. quadratic time complexity O(n²). Our technique is based on the decomposition of graphs into a set of substructures which are subsequently matched with the stable marriage algorithm. In this paper, we address the problem of comparing deformable 3D objects represented by graphs, we use a triangle-stars decomposition for triangular tessellations (graphs of 3D shapes). The proposed approach is based on computing an approximation of Graph Edit Distance which is fault-tolerant to noise and distortion which makes our method especially relevant for deformable 3D shapes comparison. We analyze and determine its time complexity. The proposed method is evaluated against benchmark databases under different evaluation criteria. Our experimental results consistently demonstrate the effectiveness and the high performances of our approach., 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), November 3-7, 2019, Abu Dhabi, United Arab Emirates

We address the problem of comparing deformable 3D objects represented by graphs such as triangular tessellations. We propose a new graph matching technique to measure the distance between these graphs. The proposed approach is based on a new decomposition of triangular tessellations into triangle-stars. The algorithm ensures a minimum number of disjoint triangle-stars, provides improved dissimilarity by covering larger neighbors and allows the creation of descriptors that are invariant or at least oblivious under the most common deformations. The present approach is based on an approximation of the Graph Edit Distance, which is fault-tolerant to noise and distortion, thus making our technique particularly suitable for the comparison of deformable objects. Classification is performed with supervised machine learning techniques. Our approach defines a metric space using graph embedding and graph kernel techniques. It is proved that the proposed distance is a pseudo-metric. Its time complexity is determined and the method is evaluated against benchmark databases. Our experimental results confirm the performances and the accuracy of our system.

Suboptimal intake of dietary vitamin C (ascorbate) increases the risk of several chronic diseases but the exact metabolic pathways affected are still unknown. In this study, we examined the metabolic profile of mice lacking the enzyme gulonolactone oxidase (Gulo) required for the biosynthesis of ascorbate. Gulo−/− mice were supplemented with 0%, 0.01%, and 0.4% ascorbate (w/v) in drinking water and serum was collected for metabolite measurements by targeted mass spectrometry. We also quantified 42 serum cytokines and examined the levels of different stress markers in liver. The metabolic profiles of Gulo−/− mice treated with ascorbate were different from untreated Gulo−/− and normal wild type mice. The cytokine profiles of Gulo−/− mice, in return, overlapped the profile of wild type animals upon 0.01% or 0.4% vitamin C supplementation. The life span of Gulo−/− mice increased with the amount of ascorbate in drinking water. It also correlated significantly with the ratios of serum arginine/lysine, tyrosine/phenylalanine, and the ratio of specific species of saturated/unsaturated phosphatidylcholines. Finally, levels of hepatic phosphorylated endoplasmic reticulum associated stress markers IRE1α and eIF2α correlated inversely with serum ascorbate and life span suggesting that vitamin C modulates endoplasmic reticulum stress response and longevity in Gulo−/− mice.

The process of continuously reallocating funds into financial assets, aiming to increase the expected return of investment and minimizing the risk, is known as portfolio management. In this paper, a portfolio management framework is developed based on a deep reinforcement learning framework called DeepBreath. The DeepBreath methodology combines a restricted stacked autoencoder and a convolutional neural network (CNN) into an integrated framework. The restricted stacked autoencoder is employed in order to conduct dimensionality reduction and features selection, thus ensuring that only the most informative abstract features are retained. The CNN is used to learn and enforce the investment policy which consists of reallocating the various assets in order to increase the expected return on investment. The framework consists of both offline and online learning strategies: the former is required to train the CNN while the latter handles concept drifts i.e. a change in the data distribution resulting from unforeseen circumstances. These are based on passive concept drift detection and online stochastic batching. Settlement risk may occur as a result of a delay in between the acquisition of an asset and its payment failing to deliver the terms of a contract. In order to tackle this challenging issue, a blockchain is employed. Finally, the performance of the DeepBreath framework is tested with four test sets over three distinct investment periods. The results show that the return of investment achieved by our approach outperforms current expert investment strategies while minimizing the market risk.

Werner syndrome (WS) is a premature aging disorder caused by mutations in a RecQ-family DNA helicase (WRN). Mice lacking part of the helicase domain of the WRN ortholog exhibit several phenotypic features of WS. In this study, we generated a Wrn mutant line that, like humans, relies entirely on dietary sources of vitamin C (ascorbate) to survive, by crossing them to mice that lack the gulonolactone oxidase enzyme required for ascorbate synthesis. In the presence of 0.01% ascorbate (w/v) in drinking water, double-mutant mice exhibited a severe reduction in lifespan, small size, sterility, osteopenia, and metabolic profiles different from wild-type (WT) mice. Although increasing the dose of ascorbate to 0.4% improved dramatically the phenotypes of double-mutant mice, the metabolic and cytokine profiles were different from age-matched WT mice. Finally, double-mutant mice treated with 0.01% ascorbate revealed a permanent activation of all the 3 branches of the ER stress response pathways due to a severe chronic oxidative stress in the ER compartment. In addition, markers associated with the ubiquitin-proteasome-dependent ER-associated degradation pathway were increased. Augmenting the dose of ascorbate reversed the activation of this pathway to WT levels rendering this pathway a potential therapeutic target in WS.-Aumailley, L., Dubois, M. J., Brennan, T. A., Garand, C., Paquet, E. R., Pignolo, R. J., Marette, A., Lebel, M. Serum vitamin C levels modulate the lifespan and endoplasmic reticulum stress response pathways in mice synthesizing a nonfunctional mutant WRN protein.

Ab initio molecular dynamics is an irreplaceable technique for the realistic simulation of complex molecular systems and processes from first principles. This paper proposes a comprehensive and self-contained review of ab initio molecular dynamics from a computational perspective and from first principles. Quantum mechanics is presented from a molecular dynamics perspective. Various approximations and formulations are proposed, including the Ehrenfest, Born–Oppenheimer, and Hartree–Fock molecular dynamics. Subsequently, the Kohn–Sham formulation of molecular dynamics is introduced as well as the afferent concept of density functional. As a result, Car–Parrinello molecular dynamics is discussed, together with its extension to isothermal and isobaric processes. Car–Parrinello molecular dynamics is then reformulated in terms of path integrals. Finally, some implementation issues are analysed, namely, the pseudopotential, the orbital functional basis, and hybrid molecular dynamics.

Proper modulation of promoter chromatin architecture is crucial for gene regulation in order to precisely and efficiently orchestrate various cellular activities. Previous studies have identified the stimulatory effect of the histone-modifying complex NuA4 on the incorporation of the histone variant H2A.Z (Htz1) at the PHO5 promoter (A. Auger, L. Galarneau, M. Altaf, A. Nourani, Y. Doyon, R. T. Utley, D. Cronier, S. Allard, and J. Côté, Mol Cell Biol 28:2257–2270, 2008, http://dx.doi.org/10.1128/MCB.01755-07 ). In vitro studies with a reconstituted system also indicated an intriguing cross talk between NuA4 and the H2A.Z-loading complex, SWR-C (M. Altaf, A. Auger, J. Monnet-Saksouk, J. Brodeur, S. Piquet, M. Cramet, N. Bouchard, N. Lacoste, R. T. Utley, L. Gaudreau, J. Côté, J Biol Chem 285:15966–15977, 2010, http://dx.doi.org/10.1074/jbc.M110.117069 ). In this work, we investigated the role of the NuA4 scaffold subunit Eaf1 in global gene expression and genome-wide incorporation of Htz1. We found that loss of Eaf1 affects Htz1 levels mostly at the promoters that are normally highly enriched in the histone variant. Analysis of eaf1 mutant cells by expression array unveiled a relationship between NuA4 and the gene network implicated in the purine biosynthesis pathway, as EAF1 deletion cripples induction of several ADE genes. NuA4 directly interacts with Bas1 activation domain, a key transcription factor of adenine genes. Chromatin immunoprecipitation (ChIP) experiments demonstrate that nucleosomes on the inactive ADE17 promoter are acetylated already by NuA4 and enriched in Htz1. Upon derepression, these poised nucleosomes respond rapidly to activate ADE gene expression in a mechanism likely reminiscent of the PHO5 promoter, leading to nucleosome disassembly. These detailed molecular events depict a specific case of cross talk between NuA4-dependent acetylation and incorporation of histone variant Htz1, presetting the chromatin structure over ADE promoters for subsequent chromatin remodeling and activated transcription.

Currently, there is no marker in use in the clinical management of colon cancer to predict which patients will respond efficiently to 5‐fluorouracil (5‐FU), a common component of all cytotoxic therapies. Our aim was to develop and validate a multigene signature associated with clinical outcome from 5‐FU therapy and to determine if it could be used to identify patients who might respond better to alternate treatments. Using a panel of 5‐FU resistant and sensitive colon cancer cell lines, we identified 103 differentially expressed genes providing us with a 5‐FU response signature. We refined this signature using a clinically relevant DNA microarray‐based dataset of 359 formalin‐fixed and paraffin‐embedded (FFPE) colon cancer samples. We then validated the final signature in an external independent DNA microarray‐based dataset of 316 stage III FFPE samples from the PETACC‐3 (Pan‐European Trails in Alimentary Tract Cancers) clinical trial. Finally, using a drug sensitivity database of 658 cell lines, we generated a list of drugs that could sensitize 5‐FU resistant patients using our signature. We confirmed using the PETACC‐3 dataset that the overall survival of subjects responding well to 5‐FU did not improve with the addition of irinotecan (FOLFIRI; two‐sided log‐rank test p = 0.795). Conversely, patients who responded poorly to 5‐FU based on our 12‐gene signature were associated with better survival on FOLFIRI therapy (one‐sided log‐rank test p = 0.039). This new multigene signature is readily applicable to FFPE samples and provides a new tool to help manage treatment in stage III colon cancer. It also provides the first evidence that a subgroup of colon cancer patients can respond better to FOLFIRI than 5‐FU treatment alone.

Increasingly, Internet of Things (IoT) domains, such as sensor networks, smart cities, and social networks, generate vast amounts of data. Such data are not only unbounded and rapidly evolving. Rather, the content thereof dynamically evolves over time, often in unforeseen ways. These variations are due to so-called concept drifts, caused by changes in the underlying data generation mechanisms. In a classification setting, concept drift causes the previously learned models to become inaccurate, unsafe and even unusable. Accordingly, concept drifts need to be detected, and handled, as soon as possible. In medical applications and emergency response settings, for example, change in behaviours should be detected in near real-time, to avoid potential loss of life. To this end, we introduce the McDiarmid Drift Detection Method (MDDM), which utilizes McDiarmid's inequality in order to detect concept drift. The MDDM approach proceeds by sliding a window over prediction results, and associate window entries with weights. Higher weights are assigned to the most recent entries, in order to emphasize their importance. As instances are processed, the detection algorithm compares a weighted mean of elements inside the sliding window with the maximum weighted mean observed so far. A significant difference between the two weighted means, upper-bounded by the McDiarmid inequality, implies a concept drift. Our extensive experimentation against synthetic and real-world data streams show that our novel method outperforms the state-of-the-art. Specifically, MDDM yields shorter detection delays as well as lower false negative rates, while maintaining high classification accuracies., Comment: 9 pages, 3 figures, 3 tables

The identification of changes in data distributions associated with data streams is critical in understanding the mechanics of data generating processes and ensuring that data models remain representative through time. To this end, concept drift detection methods often utilize statistical techniques that take numerical data as input. However, many applications produce data streams containing categorical attributes, where numerical statistical methods are not applicable. In this setting, common solutions use error monitoring, assuming that fluctuations in the error measures of a learning system correspond to concept drift. Context-based concept drift detection techniques for categorical streams, which observe changes in the actual data distribution, have received limited attention. Such context-based change detection is arguably more informative as it is data-driven and directly applicable in an unsupervised setting. This paper introduces a novel context-based algorithm for categorical data, namely FG-CDCStream. In this unsupervised method, multiple drift detection tracks are maintained and their votes are combined in order to determine whether a real change has occurred. In this way, change detections are rapid and accurate, while the number of false alarms remains low. Our experimental evaluation against synthetic data streams shows that FG-CDCStream outperforms the state-of-the art. Our analysis further indicates that FG-CDCStream produces highly accurate and representative post-change models., Series: Lecture Notes in Computer Science; no. 10558

International audience; Vaccination is the most effective course of action to prevent influenza. About 150 million doses of influenza vaccines were distributed for the 2015–2016 season in the USA alone according to the Centers for Disease Control and Prevention. Vaccine dosage is calculated based on the concentration of hemagglutinin (HA), the main surface glycoprotein expressed by influenza which varies from strain to strain. Therefore yearly-updated strain-specific antibodies and calibrating antigens are required. Preparing these quantification reagents can take up to three months and significantly slows down the release of new vaccine lots. Therefore, to circumvent the need for strain-specific sera, two anti-HA monoclonal antibodies (mAbs) against a highly conserved sequence have been produced by immunizing mice with a novel peptide-conjugate. Immunoblots demonstrate that 40 strains of influenza encompassing HA subtypes H1 to H13, as well as B strains from the Yamagata and Victoria lineage were detected when the two mAbs are combined to from a pan-HA mAb cocktail. Quantification using this pan-HA mAbs cocktail was achieved in a dot blot assay and results correlated with concentrations measured in a hemagglutination assay with a coefficient of correlation of 0.80. A competitive ELISA was also optimised with purified viral-like particles. Regardless of the quantification method used, pan-HA antibodies can be employed to accelerate process development when strain-specific antibodies are not available, and represent a valuable tool in case of pandemics. These antibodies were also expressed in CHO cells to facilitate large-scale production using bioreactor technologies which might be required tomeet industrial needs for quantification reagents. Finally, a simulation model was created to predict the binding affinity of the two anti-HA antibodies to the amino acids composing the highly conserved epitope; different probabilities of interaction between a given amino acid and the antibodies might explain the affinity of each antibody against different influenza strains.

Pathologic complete response (pCR) is a predictor for favorable outcome after neoadjuvant treatment in early breast cancer. Modulation of gene expression may also provide early readouts of biological activity and prognosis, offering the possibility for timely response‐guided treatment adjustment. The role of early transcriptional changes in predicting response to neoadjuvant chemotherapy plus bevacizumab was investigated. One‐hundred‐and‐fifty patients with large, operable and locally advanced HER2‐negative breast cancer received epirubicin and docetaxel, with the addition of bevacizumab. Patients underwent tumor biopsies at baseline, after Cycle 2 and at the time of surgery. The primary end point, pCR, and its relation with the secondary endpoints event‐free survival (EFS), overall survival (OS) and gene expression profiles, are reported. The pCR rate was 13% (95% CI 8.6–20.2), with significantly more pCRs among triple‐negative [28% (95% CI 14.8–45.4)] than among hormone receptor positive (HR+) tumors [9% (95% CI 4.6–16.3); (OR = 3.9 [CI = 1.5–10.3])]. pCR rates were not associated with EFS or OS. PAM50 subtypes significantly changed after Cycle 2 (p = 0.03) and an index of absolute changes in PAM50 correlations between these time‐points was associated with EFS [HR = 0.62 (CI = 0.3–1.1)]. In univariable analyses, signatures for angiogenesis, proliferation, estrogen receptor signaling, invasion and metastasis, and immune response, measured after Cycle 2, were associated with pCR in HR+ tumors. Evaluation of changes in molecular subtypes and other signatures early in the course of neoadjuvant treatment may be predictive of pCR and EFS. These factors may help guide further treatment and should be considered when designing neoadjuvant trials., What's new? It's a good sign for a patient's prognosis if, after pre‐operative chemotherapy, no breast cancer cells survive. But this metric isn't perfect, and varies depending on the tumor's molecular subtype. Here, the authors analyzed changes in gene expression brought on by chemotherapy. They analyzed molecular markers in biopsies from 150 breast cancer patients at three time points: baseline, after 2 cycles of neoadjuvant therapy, and right before surgery. Neoadjuvant chemotherapy significantly changed the tumor's gene expression profile, they found, and these changes could have predictive value: a bigger change between baseline and Cycle 2 correlated with longer event‐free survival.

The success of data stream mining techniques has allowed decision makers to analyze their data in multiple domains, ranging from monitoring network intrusion to financial markets analysis and online sales transactions exploration. Specifically, online ensembles that construct accurate models against drifting data streams have been developed. Recently, there has been a surge in interest in mobile (or so-called pocket) data stream mining, aiming to construct near real-time models for data stream mining applications that run on mobile devices. In such a setting, it follows that the computational resources are limited and that there is a need to adapt analytics to map the resource usage requirements. Consequently, the resultant models should not only be highly accurate, but they should also adapt swiftly to changes. In addition, the data mining techniques should be fast, scalable, and efficient in terms of resource allocation. It then becomes important to consider Return on Investment (ROI) issues such as storage requirements and memory utilization. This paper introduces the Adaptive Ensemble Size (AES) algorithm, an extension of the Online Bagging method, to address these issues. Our AES method dynamically adapts the sizes of ensembles, based on ROI usage patterns. We illustrate our approach by analyzing the performances against both synthetic and real-world data streams. The results, when comparing our AES algorithm with the state-of-the-art, indicate that we are able to obtain a high Return on Investment (ROI) and to swiftly adapt to change, without compromising on the predictive accuracy.

Background The ability to reliably identify the state (activated, repressed, or latent) of any molecular process in the tumor of a patient from an individual whole-genome gene expression profile obtained from microarray or RNA sequencing (RNA-seq) promises important clinical utility. Unfortunately, all previous bioinformatics tools are only applicable in large and diverse panels of patients, or are limited to a single specific pathway/process (e.g. proliferation). Methods Using a panel of 4510 whole-genome gene expression profiles from 10 different studies we built and selected models predicting the activation status of a compendium of 1733 different biological processes. Using a second independent validation dataset of 742 patients we validated the final list of 1773 models to be included in a de novo tool entitled absolute inference of patient signatures (AIPS). We also evaluated the prognostic significance of the 1773 individual models to predict outcome in all and in specific breast cancer subtypes. Results We described the development of the de novo tool entitled AIPS that can identify the activation status of a panel of 1733 different biological processes from an individual breast cancer microarray or RNA-seq profile without recourse to a broad cohort of patients. We demonstrated that AIPS is stable compared to previous tools, as the inferred pathway state is not affected by the composition of a dataset. We also showed that pathway states inferred by AIPS are in agreement with previous tools but use far fewer genes. We determined that several AIPS-defined pathways are prognostic across and within molecularly and clinically define subtypes (two-sided log-rank test false discovery rate (FDR)

The ability to reliably identify the state (activated, repressed, or latent) of any molecular process in the tumor of a patient from an individual whole-genome gene expression profile obtained from microarray or RNA sequencing (RNA-seq) promises important clinical utility. Unfortunately, all previous bioinformatics tools are only applicable in large and diverse panels of patients, or are limited to a single specific pathway/process (e.g. proliferation). Using a panel of 4510 whole-genome gene expression profiles from 10 different studies we built and selected models predicting the activation status of a compendium of 1733 different biological processes. Using a second independent validation dataset of 742 patients we validated the final list of 1773 models to be included in a de novo tool entitled absolute inference of patient signatures (AIPS). We also evaluated the prognostic significance of the 1773 individual models to predict outcome in all and in specific breast cancer subtypes. We described the development of the de novo tool entitled AIPS that can identify the activation status of a panel of 1733 different biological processes from an individual breast cancer microarray or RNA-seq profile without recourse to a broad cohort of patients. We demonstrated that AIPS is stable compared to previous tools, as the inferred pathway state is not affected by the composition of a dataset. We also showed that pathway states inferred by AIPS are in agreement with previous tools but use far fewer genes. We determined that several AIPS-defined pathways are prognostic across and within molecularly and clinically define subtypes (two-sided log-rank test false discovery rate (FDR)

SUMMARY All living organisms are continuously faced with endogenous or exogenous stress conditions affecting genome stability. DNA repair pathways act as a defense mechanism, which is essential to maintain DNA integrity. There is much to learn about the regulation and functions of these mechanisms, not only in human cells but also equally in divergent organisms. In trypanosomatids, DNA repair pathways protect the genome against mutations but also act as an adaptive mechanism to promote drug resistance. In this review, we scrutinize the molecular mechanisms and DNA repair pathways which are conserved in trypanosomatids. The recent advances made by the genome consortiums reveal the complete genomic sequences of several pathogens. Therefore, using bioinformatics and genomic sequences, we analyze the conservation of DNA repair proteins and their key protein motifs in trypanosomatids. We thus present a comprehensive view of DNA repair processes in trypanosomatids at the crossroads of DNA repair and drug resistance.

Great advances in analytical technology coupled with accelerated new drug development and growing understanding of biological challenges, such as tumor heterogeneity, have required a change in the focus for biobanking. Most current banks contain samples of primary tumors, but linking molecular signatures to therapeutic questions requires serial biopsies in the setting of metastatic disease, next-generation of biobanking. Furthermore, an integration of multidimensional analysis of various molecular components, that is, RNA, DNA, methylome, microRNAome and post-translational modifications of the proteome, is necessary for a comprehensive view of a tumor's biology. While data using such biopsies are now regularly presented, the preanalytical variables in tissue procurement and processing in multicenter studies are seldom detailed and therefore are difficult to duplicate or standardize across sites and across studies. In the context of a biopsy-driven clinical trial, we generated a detailed protocol that includes morphological evaluation and isolation of high-quality nucleic acids from small needle core biopsies obtained from liver metastases. The protocol supports stable shipping of samples to a central laboratory, where biopsies are subsequently embedded in support media. Designated pathologists must evaluate all biopsies for tumor content and macrodissection can be performed if necessary to meet our criteria of >60% neoplastic cells and

Histone acetyltransferases (HATs) assemble into multisubunit complexes in order to target distinct lysine residues on nucleosomal histones. Here, we characterize native HAT complexes assembled by the BRPF family of scaffold proteins. Their plant homeodomain (PHD)–Zn knuckle–PHD domain is essential for binding chromatin and is restricted to unmethylated H3K4, a specificity that is reversed by the associated ING subunit. Native BRPF1 complexes can contain either MOZ/MORF or HBO1 as catalytic acetyltransferase subunit. Interestingly, while the previously reported HBO1 complexes containing JADE scaffold proteins target histone H4, the HBO1–BRPF1 complex acetylates only H3 in chromatin. We mapped a small region to the N terminus of scaffold proteins responsible for histone tail selection on chromatin. Thus, alternate choice of subunits associated with HBO1 can switch its specificity between H4 and H3 tails. These results uncover a crucial new role for associated proteins within HAT complexes, previously thought to be intrinsic to the catalytic subunit.

Objective To characterize at high resolution the DNA methylation changes which occur in the genome of serous epithelial ovarian cancer (EOC) in association with tumor aggressiveness. Methods Methylated DNA immunoprecipitation in combination with CpG island-tiling arrays was used to compare the methylation profiles of five borderline, five grade 1/stage III/IV, five grade 3/stage I and five grade 3/stage III/IV serous EOC tumors, to those of five normal human ovarian tissue samples. Results We found widespread DNA hypermethylation that occurs even in low-malignant potential (borderline) tumors and which predominantly includes key developmental/homeobox genes. Contrary to DNA hypermethylation, significant DNA hypomethylation was observed only in grade 3 serous EOC tumors. The latter observation was further confirmed when comparing the DNA methylation profiles of primary cell cultures derived from matched tumor samples obtained prior to, and following chemotherapy treatment from two serous EOC patients with advanced disease. To our knowledge this is the first report that has shown the presence of massive DNA hypomethylation in advanced serous EOC, associated with tumor malignancy and disease progression. Conclusions Our data raise the concern that demethylating drugs that are currently being used in advanced EOC disease (representing the majority of serous EOC cases) might have adverse effects due to activation of oncogenes and prometastatic genes. Understanding the relative roles of hypomethylation and hypermethylation in cancer could have clear implications on the therapeutic use of agents targeting the DNA methylation machinery.

The comparison of macromolecular structures, in terms of functionalities, is a crucial step when aiming to identify potential docking sites. Drug designers require the identification of such docking sites for the binding of two proteins, in order to form a stable complex. This paper presents a review of current approaches to macromolecular structure comparison and docking, following an algorithmic approach. We describe techniques based on the Bayesian framework, kernel-based methods, projection-based techniques and spectral approaches. We introduce the use of quantum particle swarm optimization, in order to aid us to find the most appropriate docking sites. We discuss the importance of the heat and Schrodinger equations to address the non-rigid nature of proteins and highlight the strengths and limitations of the various methods.

Circadian clocks are autonomous daily timekeeping mechanisms that allow organisms to adapt to environmental rhythms as well as temporally organize biological functions. Clock-controlled timekeeping involves extensive regulation of rhythmic gene expression. To date, relatively few clock-associated promoter elements have been identified and characterized. In an unbiased search of core clock gene promoters from 12 species of Drosophila, we discovered a 29-bp consensus sequence that we designated as the Clock-Associated Transcriptional Activation Cassette or ‘CATAC’. To experimentally address the spatiotemporal expression information associated with this element, we generated constructs with four separate native CATAC elements upstream of a basal promoter driving expression of either the yeast Gal4 or firefly luciferase reporter genes. Reporter assays showed that presence of wild-type, but not mutated CATAC elements, imparted increased expression levels as well as rhythmic regulation. Part of the CATAC consensus sequence resembles the E-box binding site for the core circadian transcription factor CLOCK/CYCLE (CLK/CYC), and CATAC-mediated expression rhythms are lost in the presence of null mutations in either cyc or the gene encoding the CLK/CYC inhibitor, period (per). Nevertheless, our results indicate that CATAC's enhancer function persists in the absence of CLK/CYC. Thus, CATAC represents a novel cis-regulatory element encoding clock-controlled regulation.

Data streams, where an instance is only seen once and where a limited amount of data can be buffered for processing at a later time, are omnipresent in today’s real-world applications. In this context, adaptive online ensembles that are able to learn incrementally have been developed. However, the issue of handling data that arrives asynchronously has not received enough attention. Often, the true class label arrives after with a time-lag, which is problematic for existing adaptive learning techniques. It is not realistic to require that all class labels be made available at training time. This issue is further complicated by the presence of late-arriving, slowly changing dimensions (i.e., late-arriving descriptive attributes). The aim of active learning is to construct accurate models when few labels are available. Thus, active learning has been proposed as a way to obtain such missing labels in a data stream classification setting. To this end, this paper introduces an active online ensemble (AOE) algorithm that extends online ensembles with an active learning component. Our experimental results demonstrate that our AOE algorithm builds accurate models against much smaller ensemble sizes, when compared to traditional ensemble learning algorithms. Further, our models are constructed against small, incremental data sets, thus reducing the number of examples that are required to build accurate ensembles., 8th International Conference on Knowledge Discovery and Information Retrieval, November 9-11, 2016, Porto, Portugal

Adaptive online learning algorithms have been successfully applied to fast-evolving data streams. Such streams are susceptible to concept drift, which implies that the most suitable type of classifier often changes over time. In this setting, a system that is able to seamlessly select the type of learner that presents the current “best” model holds much value. For example, in a scenario such as user profiling for security applications, model adaptation is of the utmost importance. We have implemented a multi-strategy framework, the so-called Tornado environment, which is able to run multiple and diverse classifiers simultaneously for decision making. In our framework, the current learner with the highest performance, at a specific point in time, is selected and the corresponding model is then provided to the user. In our implementation, we employ an Error-Memory-Runtime (EMR) measure which combines the error-rate, the memory usage and the runtime of classifiers as a performance indicator. We conducted experiments on synthetic and real-world datasets with the Hoeffding Tree, Naive Bayes, Perceptron, K-Nearest Neighbours and Decision Stumps algorithms. Our results indicate that our environment is able to adapt to changes and to continuously select the best current type of classifier, as the data evolve.

Online ensemble methods have been very successful to create accurate models against data streams that are susceptible to concept drift. The success of data stream mining has allowed diverse users to analyse their data in multiple domains, ranging from monitoring stock markets to analysing network traffic and exploring ATM transactions. Increasingly, data stream mining applications are running on mobile devices, utilizing the variety of data generated by sensors and network technologies. Subsequently, there has been a surge in interest in mobile (or so-called pocket) data stream mining, aiming to construct near real-time models. However, it follows that the computational resources are limited and that there is a need to adapt analytics to map the resource usage requirements. In this context, the resultant models produced by such algorithms should thus not only be highly accurate and be able to swiftly adapt to changes. Rather, the data mining techniques should also be fast, scalable, and efficient in terms of resource allocation. It then becomes important to consider Return on Investment (ROI) issues such as storage space needs and memory utilization. This paper introduces the Adaptive Ensemble Size (AES) algorithm, an extension of the Online Bagging method, to address this issue. Our AES method dynamically adapts the sizes of ensembles, based on the most recent memory usage requirements. Our results when comparing our AES algorithm with the state-of-the-art indicate that we are able to obtain a high Return on Investment (ROI) without compromising on the accuracy of the results., 4th International Workshop on New Frontiers in Mining Complex Patterns, September 7, 2015, Porto, Portugal, Series: Lecture Notes in Computer Science; no. 9607

Small non-coding microRNAs are believed to be involved in the mechanism of aging but nothing is known on the impact of microRNAs in the progeroid disorder Werner syndrome (WS). WS is a premature aging disorder caused by mutations in a RecQ-like DNA helicase. Mice lacking the helicase domain of the WRN ortholog exhibit many phenotypic features of WS, including a pro-oxidant status and a shorter mean life span. Caenorhabditis elegans (C. elegans) with a nonfunctional wrn-1 DNA helicase also exhibit a shorter life span. Thus, both models are relevant to study the expression of microRNAs involved in WS. In this study, we show that miR-124 expression is lost in the liver of Wrn helicase mutant mice. Interestingly, the expression of this conserved miR-124 in whole wrn-1 mutant worms is also significantly reduced. The loss of mir-124 in C. elegans increases reactive oxygen species formation and accumulation of the aging marker lipofuscin, reduces whole body ATP levels and results in a reduction in life span. Finally, supplementation of vitamin C normalizes the median life span of wrn-1 and mir-124 mutant worms. These results suggest that biological pathways involving WRN and miR-124 are conserved in the aging process across different species.

Endothelial cell migration induced in response to vascular endothelial growth factor (VEGF) is an essential step of angiogenesis. It depends in part on the activation of the p38/MAPKAP kinase-2/LIMK1/annexin-A1 (ANXA1) signaling axis. In the present study, we obtained evidence indicating that miR-196a specifically binds to the 3'-UTR region of ANXA1 mRNA to repress its expression. In accordance with the role of ANXA1 in cell migration and angiogenesis, the ectopic expression of miR-196a is associated with decreased cell migration in wound closure assays, and the inhibitory effect of miR-196a is rescued by overexpressing ANXA1. This finding highlights the fact that ANXA1 is a required mediator of VEGF-induced cell migration. miR-196a also reduces the formation of lamellipodia in response to VEGF suggesting that ANXA1 regulates cell migration by securing the formation of lamellipodia at the leading edge of the cell. Additionally, in line with the fact that cell migration is an essential step of angiogenesis, the ectopic expression of miR-196a impairs the formation of capillary-like structures in a tissue-engineered model of angiogenesis. Here again, the effect of miR-196a is rescued by overexpressing ANXA1. Moreover, the presence of miR-196a impairs the VEGF-induced in vivo neo-vascularization in the Matrigel Plug assay. Interestingly, VEGF reduces the expression of miR-196a, which is associated with an increased level of ANXA1. Similarly, the inhibition of miR-196a with an antagomir results in an increased level of ANXA1. We conclude that the VEGF-induced decrease of miR-196a expression may participate to the angiogenic switch by maintaining the expression of ANXA1 to levels required to enable p38-ANXA1-dependent endothelial cell migration and angiogenesis in response to VEGF.

Data mining has been successfully applied in many businesses, thus aiding managers to make informed decisions that are based on facts, rather than having to rely on guesswork and incorrect extrapolations. Data mining algorithms equip institutions to predict the movements of financial indicators, enable companies to move towards more energy-efficient buildings, as well as allow businesses to conduct targeted marketing campaigns and forecast sales. Specific data mining success stories include customer loyalty prediction, economic forecasting, and fraud detection. The strength of data mining lies in the fact that it allows for not only predicting trends and behaviors, but also for the discovery of previously unknown patterns. However, a number of challenges remain, especially in this era of big data. These challenges are brought forward due to the sheer Volume of today’s databases, as well as the Velocity (in terms of speed of arrival) and the Variety, in terms of the various types of data collected. This chapter focuses on techniques that address these issues. Specifically, we turn our attention to the financial sector, which has become paramount to business. Our discussion centers on issues such as considering data distributions with high fluctuations, incorporating late arriving data, and handling the unknown. We review the current state-of-the-art, mainly focusing on model-based approaches. We conclude the chapter by providing our perspective as to what the future holds, in terms of building accurate models against today’s business, and specifically financial, data.

There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database. Category: Smart and intelligent computing