title: Biomarker identification by knowledge-driven multilevel ICA and motif analysis.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. lchen06@vt.edu
authors.forename: Li
authors.initials: L
authors.lastname: Chen
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: Chen
authors.initials: C
authors.lastname: Wang
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.forename: Ie-Ming
authors.initials: IeM
authors.lastname: Shih
authors.forename: Tian-Li
authors.initials: TL
authors.lastname: Wang
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
authors.forename: Eric P
authors.initials: EP
authors.lastname: Hoffman
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis* / Q000737:chemistry; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis; D025341:Principal Component Analysis
chemical_list: D014408:Biomarkers, Tumor