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    Oxford University Press
    Publication Date: 2013-10-04
    Description: : BioPAX is a community-developed standard language for biological pathway data. A key functionality required for efficient BioPAX data exchange is validation— detecting errors and inconsistencies in BioPAX documents. The BioPAX Validator is a command-line tool, Java library and online web service for BioPAX that performs 〉100 classes of consistency checks. Availability and implementation: The validator recognizes common syntactic errors and semantic inconsistencies and reports them in a customizable human readable format. It can also automatically fix some errors and normalize BioPAX data. Since its release, the validator has become a critical tool for the pathway informatics community, detecting thousands of errors and helping substantially increase the conformity and uniformity of BioPAX-formatted data. The BioPAX Validator is open source and released under LGPL v3 license. All sources, binaries and documentation can be found at sf.net/p/biopax, and the latest stable version of the web application is available at biopax.org/validator. Contact: igor.rodchenkov@utoronto.ca or gary.bader@utoronto.ca
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 3
    Publication Date: 2016-06-16
    Description: Motivation: Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein–protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins. Results: A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein–protein interactions in S. cerevisae using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy of 0.97. Availability and implementation: Do main- Mo tif Mediated Interaction Pred iction (DoMo-Pred) command line tool and all relevant datasets are available under GNU LGPL license for download from http://www.baderlab.org/Software/DoMo-Pred . The DoMo-Pred command line tool is implemented using Python 2.7 and C ++. Contact: gary.bader@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 4
    Publication Date: 2015-01-21
    Description: Glioblastoma (GBM) is a cancer comprised of morphologically, genetically, and phenotypically diverse cells. However, an understanding of the functional significance of intratumoral heterogeneity is lacking. We devised a method to isolate and functionally profile tumorigenic clones from patient glioblastoma samples. Individual clones demonstrated unique proliferation and differentiation abilities. Importantly, naïve...
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 5
    Publication Date: 2012-04-11
    Description: Human Epidermal Growth Factor Receptor 2-positive (HER2+) breast cancer (BC) is a highly aggressive disease commonly treated with chemotherapy and anti-HER2 drugs, including trastuzumab. There is currently no way to predict which HER2+ BC patients will benefit from these treatments. Previous prognostic signatures for HER2+ BC were developed irrespective of the subtype or the hierarchical organization of cancer in which only a fraction of cells, tumor-initiating cells (TICs), can sustain tumor growth. Here, we used serial dilution and single-cell transplantation assays to identify MMTV-Her2/Neu mouse mammary TICs as CD24+:JAG1− at a frequency of 2–4.5%. A 17-gene Her2-TIC-enriched signature (HTICS), generated on the basis of differentially expressed genes in TIC versus non-TIC fractions and trained on one HER2+ BC cohort, predicted clinical outcome on multiple independent HER2+ cohorts. HTICS included up-regulated genes involved in S/G2/M transition and down-regulated genes involved in immune response. Its prognostic power was independent of other predictors, stratified lymph node+ HER2+ BC into low and high-risk subgroups, and was specific for HER2+:estrogen receptor alpha-negative (ERα−) patients (10-y overall survival of 83.6% for HTICS− and 24.0% for HTICS+ tumors; hazard ratio = 5.57; P = 0.002). Whereas HTICS was specific to HER2+:ERα− tumors, a previously reported stroma-derived signature was predictive for HER2+:ERα+ BC. Retrospective analyses revealed that patients with HTICS+ HER2+:ERα− tumors resisted chemotherapy but responded to chemotherapy plus trastuzumab. HTICS is, therefore, a powerful prognostic signature for HER2+:ERα− BC that can be used to identify high risk patients that would benefit from anti-HER2 therapy.
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 6
    Publication Date: 2013-02-13
    Description: : The characterization of the complex phenomenon of cell differentiation is a key goal of both systems and computational biology. G e S to D ifferent is a Cytoscape plugin aimed at the generation and the identification of gene regulatory networks (GRNs) describing an arbitrary stochastic cell differentiation process. The (dynamical) model adopted to describe general GRNs is that of noisy random Boolean networks (NRBNs), with a specific focus on their emergent dynamical behavior. G e S to D ifferent explores the space of GRNs by filtering the NRBN instances inconsistent with a stochastic lineage differentiation tree representing the cell lineages that can be obtained by following the fate of a stem cell descendant. Matched networks can then be analyzed by Cytoscape network analysis algorithms or, for instance, used to define (multiscale) models of cellular dynamics. Availability: Freely available at http://bimib.disco.unimib.it/index.php/Retronet#GESTODifferent or at the Cytoscape App Store http://apps.cytoscape.org/ . Contact: marco.antoniotti@unimib.it
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 7
    Publication Date: 2019
    Description: 〈p〉Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine-learning approaches across most cancer types. Compared to traditional machine-learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows.〈/p〉
    Electronic ISSN: 1744-4292
    Topics: Biology
    Published by EMBO Press
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  • 8
    Publication Date: 2012-03-29
    Description: Peptide recognition domains and transcription factors play crucial roles in cellular signaling. They bind linear stretches of amino acids or nucleotides, respectively, with high specificity. Experimental techniques that assess the binding specificity of these domains, such as microarrays or phage display, can retrieve thousands of distinct ligands, providing detailed insight into binding specificity. In particular, the advent of next-generation sequencing has recently increased the throughput of such methods by several orders of magnitude. These advances have helped reveal the presence of distinct binding specificity classes that co-exist within a set of ligands interacting with the same target. Here, we introduce a software system called MUSI that can rapidly analyze very large data sets of binding sequences to determine the relevant binding specificity patterns. Our pipeline provides two major advances. First, it can detect previously unrecognized multiple specificity patterns in any data set. Second, it offers integrated processing of very large data sets from next-generation sequencing machines. The results are visualized as multiple sequence logos describing the different binding preferences of the protein under investigation. We demonstrate the performance of MUSI by analyzing recent phage display data for human SH3 domains as well as microarray data for mouse transcription factors.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 9
    Publication Date: 2014-07-19
    Description: : Correlating disease mutations with clinical and phenotypic information such as drug response or patient survival is an important goal of personalized cancer genomics and a first step in biomarker discovery. HyperModules is a network search algorithm that finds frequently mutated gene modules with significant clinical or phenotypic signatures from biomolecular interaction networks. Availability and implementation: HyperModules is available in Cytoscape App Store and as a command line tool at www.baderlab.org/Sofware/HyperModules . Contact: Juri.Reimand@utoronto.ca or Gary.Bader@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 10
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    Unknown
    Oxford University Press
    Publication Date: 2013-05-16
    Description: : Cytoscape is an open source software tool for biological network visualization and analysis, which can be extended with independently developed apps. We launched the Cytoscape App Store to highlight the important features that apps add to Cytoscape, enable researchers to find and install apps they need and help developers promote their apps. Availability: The App Store is available at http://apps.cytoscape.org . Contact: apico@gladstone.ucsf.edu
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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