Article Abstract

	Title:	Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis
	Author:	Li Chen, Jianhua Xuan, Yue Wang, Eric P. Hoffman, Rebecca B. Riggins, Robert Clarke
	Address:	Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. ' Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. ' Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. ' Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC 20010, USA. ' Department of Oncology an Physiology & Biophysics, Georgetown University, School of Medicine, Washington, DC 20057, USA. ' Department of Oncology an Physiology & Biophysics, Georgetown University, School of Medicine, Washington, DC 20057, USA
	Journal:	International Journal of Computational Biology and Drug Design 2009 - Vol. 2, No.1  pp. 1 - 20
	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.
	Keywords:	transcription regulatory modules; motif enrichment analysis; SVR; support vector regression; statistical significance analysis; multi-level regulator identification; multi-level motifs; mRNA expression analysis; false positives; gene regulatory modules; regulatory module identification; motif binding; gene expression data; yeast cell cycle; breast cancer microarray.
	DOI:	10.1504/IJCBDD.2009.027582
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