Biomarker identification by knowledge-driven multilevel ICA and motif analysis

Li Chen, Jianhua Xuan, Chen Wang, Yue Wang, Le Ming Shih, Tian Li Wang, Zhen Zhang, Robert Clarke, Eric P. Hoffman

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)365-381
Number of pages17
JournalInternational Journal of Data Mining and Bioinformatics
Issue number4
StatePublished - 2009


  • Biomarker identification
  • Gene clustering
  • Gene regulatory networks
  • ICA
  • Independent component analysis
  • Microarray data analysis
  • Motif analysis
  • Multi-level ICA

ASJC Scopus subject areas

  • Information Systems
  • General Biochemistry, Genetics and Molecular Biology
  • Library and Information Sciences


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