Learning from biomedical linked data to suggest valid pharmacogenes View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Kevin Dalleau, Yassine Marzougui, Sébastien Da Silva, Patrice Ringot, Ndeye Coumba Ndiaye, Adrien Coulet

ABSTRACT

BACKGROUND: A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available. METHOD: We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases. One of the advantages of linked data is that they are built on a standard framework that facilitates the joint use of various sources, and thus facilitates considering features of various origins. We propose a selection and linkage of data sources relevant to pharmacogenomics, including for example DisGeNET and Clinvar. We use machine learning to identify and prioritize pharmacogenes that are the most probably valid, considering the selected linked data. This identification relies on the classification of gene-drug pairs as either pharmacogenomically associated or not and was experimented with two machine learning methods -random forest and graph kernel-, which results are compared in this article. RESULTS: We assembled a set of linked data relative to pharmacogenomics, of 2,610,793 triples, coming from six distinct resources. Learning from these data, random forest enables identifying valid pharmacogenes with a F-measure of 0.73, on a 10 folds cross-validation, whereas graph kernel achieves a F-measure of 0.81. A list of top candidates proposed by both approaches is provided and their obtention is discussed. More... »

PAGES

16

References to SciGraph publications

  • 2009-04. gBoost: a mathematical programming approach to graph classification and regression in MACHINE LEARNING
  • 2010. Interactive Relationship Discovery via the Semantic Web in THE SEMANTIC WEB: RESEARCH AND APPLICATIONS
  • 2011. Link Prediction for Annotation Graphs Using Graph Summarization in THE SEMANTIC WEB – ISWC 2011
  • 2011. Relational Kernel Machines for Learning from Graph-Structured RDF Data in THE SEMANTIC WEB: RESEARCH AND APPLICATIONS
  • 2010. MIForests: Multiple-Instance Learning with Randomized Trees in COMPUTER VISION – ECCV 2010
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2012. Graph Kernels for RDF Data in THE SEMANTIC WEB: RESEARCH AND APPLICATIONS
  • 2011. Multivariate Prediction for Learning on the Semantic Web in INDUCTIVE LOGIC PROGRAMMING
  • 2012-10. Pharmacogenomics Knowledge for Personalized Medicine in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2011-12. Linked open drug data for pharmaceutical research and development in JOURNAL OF CHEMINFORMATICS
  • 2013. A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2009. Discovering and Maintaining Links on the Web of Data in THE SEMANTIC WEB - ISWC 2009
  • 2013. Bio2RDF Release 2: Improved Coverage, Interoperability and Provenance of Life Science Linked Data in THE SEMANTIC WEB: SEMANTICS AND BIG DATA
  • 2011-12. Integration and publication of heterogeneous text-mined relationships on the Semantic Web in JOURNAL OF BIOMEDICAL SEMANTICS
  • 2011-03-15. Ontology-Based Knowledge Discovery in Pharmacogenomics in SOFTWARE TOOLS AND ALGORITHMS FOR BIOLOGICAL SYSTEMS
  • 2009-08. Generating Genome‐Scale Candidate Gene Lists for Pharmacogenomics in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2012-04. Three-gene predictor of clinical outcome for gastric cancer patients treated with chemotherapy in THE PHARMACOGENOMICS JOURNAL
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13326-017-0125-1

    DOI

    http://dx.doi.org/10.1186/s13326-017-0125-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1084954536

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28427468


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