A novel information retrieval model for high-throughput molecular medicine modalities

Firas H. Wehbe, Steven H. Brown, Pierre P. Massion, Cynthia S. Gadd, Daniel R. Masys, Constantin F. Aliferis

    Research output: Contribution to journalReview article

    1 Scopus citations

    Abstract

    Significant research has been devoted to predicting diagnosis, prognosis, and response to treatment using high-throughput assays. Rapid translation into clinical results hinges upon efficient access to up-to-date and high-quality molecular medicine modalities. We first explain why this goal is inadequately supported by existing databases and portals and then introduce a novel semantic indexing and information retrieval model for clinical bioinformatics. The formalism provides the means for indexing a variety of relevant objects (e.g. papers, algorithms, signatures, datasets) and includes a model of the research processes that creates and validates these objects in order to support their systematic presentation once retrieved. We test the applicability of the model by constructing proof-of-concept encodings and visual presentations of evidence and modalities in molecular profiling and prognosis of: (a) diffuse large B-cell lymphoma (DLBCL) and (b) breast cancer.

    Original languageEnglish (US)
    Pages (from-to)1-17
    Number of pages17
    JournalCancer Informatics
    Volume8
    StatePublished - Dec 1 2009

      Fingerprint

    Keywords

    • Clinical bioinformatics
    • Information retrieval
    • Molecular medicine
    • Predictive computational models
    • Semantic model

    ASJC Scopus subject areas

    • Oncology
    • Cancer Research

    Cite this

    Wehbe, F. H., Brown, S. H., Massion, P. P., Gadd, C. S., Masys, D. R., & Aliferis, C. F. (2009). A novel information retrieval model for high-throughput molecular medicine modalities. Cancer Informatics, 8, 1-17.