Text and data mining for biomedical discovery

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

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

    1 Scopus citations


    Text and data mining methods constantly advance and are applied in different fields. In order for them to impact the biomedical discovery process, it is necessary to thoroughly engage scientists at both ends, and conduct thorough empirical evaluations as to their ability to suggest novel hypotheses and address the most crucial questions. The PSB 2014 Session on Text and Data Mining for Biomedical Discovery presents eight papers that advance the field in this mutually reinforcing fashion. Work presented in this session includes data mining and analysis techniques that are applicable to a broad spectrum of problems, including the analysis and visualization of mass spectrometry based proteomics data and longitudinal data, as well as gene function, protein function and protein fold prediction. Text mining approaches selected for presentation include a method for predicting genes involved in disease or in drug response, a method for extracting events relevant to biological pathways, and an approach that mixes text and data mining techniques to predict important milestones in the female reproductive lifespan.

    Original languageEnglish (US)
    Title of host publication19th Pacific Symposium on Biocomputing, PSB 2014
    PublisherWorld Scientific Publishing Co. Pte Ltd
    Number of pages4
    ISBN (Print)9789814596343
    StatePublished - 2014
    Event19th Pacific Symposium on Biocomputing, PSB 2014 - Kohala Coast, United States
    Duration: Jan 3 2014Jan 7 2014


    Other19th Pacific Symposium on Biocomputing, PSB 2014
    Country/TerritoryUnited States
    CityKohala Coast

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Biomedical Engineering


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