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Int J Data Min Bioinform. 2009;3(4):365-81.

Biomarker identification by knowledge-driven multilevel ICA and motif analysis.

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1
Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. lchen06@vt.edu

Abstract

Traditional statistical methods often fail to identify biologically meaningful biomarkers from expression data alone. In this paper, we develop a novel strategy, namely knowledge-driven multi-level Independent Component Analysis (ICA), to infer regulatory signals and identify biomarkers based on clustering results and partial prior knowledge. A statistical test is designed to evaluate significance of transcription factor enrichment for extracted gene set based on motif information. The experimental results on an Rsf-1 (HBXAP) induced microarray data set show that our method can successfully extract biologically meaningful biomarkers related to ovarian cancer compared to other gene selection methods with or without prior knowledge.

PMID:
20052902
[Indexed for MEDLINE]
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