Text and data mining for biomedical discovery

Graciela Gonzalez, Kevin Bretonnel Cohen, Maricel G. Kann, Casey S. Greene, Robert Leaman, Udo Hahn, Nigam Shah, Jieping Ye

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction. Text mining advances include a classification method to determine whether a published article contains pharmacological experiments relevant to drug-drug interactions, a fine-grained text mining approach for detecting the catalytic sites in proteins in the biomedical literature, and a method for automatically extending a taxonomy of health related terms to integrate consumer-friendly synonyms for medical terminologies.

    Original languageEnglish (US)
    Title of host publication18th Pacific Symposium on Biocomputing, PSB 2013
    PublisherWorld Scientific Publishing Co. Pte Ltd
    ISBN (Print)9781627480161
    StatePublished - 2013
    Event18th Pacific Symposium on Biocomputing, PSB 2013 - Kohala Coast, United States
    Duration: Jan 3 2013Jan 7 2013

    Other

    Other18th Pacific Symposium on Biocomputing, PSB 2013
    CountryUnited States
    CityKohala Coast
    Period1/3/131/7/13

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Biomedical Engineering

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