Firas H. Wehbe¹, Steven H. Brown^1,2, Pierre P. Massion³, Cynthia S. Gadd¹, Daniel R. Masys¹ and Constantin F. Aliferis^1,4,5

¹Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, U.S.A. ²U.S. Department of Veteran Affairs, U.S.A. ³Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, U.S.A. ⁴Center of Health Informatics and Bioinformatics, New York University. ⁵Department of Pathology, New York University School of Medicine

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.