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 language | English (US) |
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Pages (from-to) | 1-17 |
Number of pages | 17 |
Journal | Cancer Informatics |
Volume | 8 |
DOIs | |
State | Published - 2009 |
Keywords
- Clinical bioinformatics
- Information retrieval
- Molecular medicine
- Predictive computational models
- Semantic model
ASJC Scopus subject areas
- Oncology
- Cancer Research