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Cancer Inform. 2009 Feb 9;8:1-17.

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

Author information

1
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. firas.wehbe@vanderbilt.edu

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.

KEYWORDS:

clinical bioinformatics; information retrieval; molecular medicine; predictive computational models; semantic model

PMID:
19458790
PMCID:
PMC2664697
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