Biomarker identification by knowledge-driven multilevel ICA and motif analysis Online publication date: Mon, 09-Nov-2009
by Li Chen, Jianhua Xuan, Chen Wang, Yue Wang, Ie-Ming Shih, Tian-Li Wang, Zhen Zhang, Robert Clarke, Eric P. Hoffman
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 3, No. 4, 2009
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.
Online publication date: Mon, 09-Nov-2009
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