Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis

Li Chen, Jianhua Xuan, Yue Wang, Eric P. Hoffman, Rebecca B. Riggins, Robert Clarke

Research output: Contribution to journalArticlepeer-review

Abstract

Many computational methods for identification of transcription regulatory modules often result in many false positives in practice due to noise sources of binding information and gene expression profiling data. In this paper, we propose a multi-level strategy for condition-specific gene regulatory module identification by integrating motif binding information and gene expression data through support vector regression and significant analysis. We have demonstrated the feasibility of the proposed method on a yeast cell cycle data set. The study on a breast cancer microarray data set shows that it can successfully identify the significant and reliable regulatory modules associated with breast cancer.

Original languageEnglish (US)
Pages (from-to)1-20
Number of pages20
JournalInternational Journal of Computational Biology and Drug Design
Volume2
Issue number1
DOIs
StatePublished - Aug 2009

Keywords

  • Motif enrichment analysis
  • Multi-level regulator identification
  • SVR
  • Statistical significance analysis
  • Support vector regression
  • Transcription regulatory module

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications

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