Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis.
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.
Chen
Li
L
Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. lchen06@vt.edu
Xuan
Jianhua
J
Wang
Yue
Y
Hoffman
Eric P
EP
Riggins
Rebecca B
RB
Clarke
Robert
R
D001943:Breast Neoplasms / Q000235:genetics; D002453:Cell Cycle / Q000235:genetics; D045744:Cell Line, Tumor; D019295:Computational Biology; D030561:Databases, Nucleic Acid; D005260:Female; D005656:Fungal Proteins / Q000235:genetics; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D015966:Gene Expression Regulation, Fungal; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics; D012331:RNA, Fungal / Q000235:genetics; D012333:RNA, Messenger / Q000235:genetics*; D012334:RNA, Neoplasm / Q000235:genetics; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics; D012985:Software Design; D014157:Transcription Factors / Q000235:genetics; D015533:Transcriptional Activation
D005656:Fungal Proteins; D009363:Neoplasm Proteins; D012331:RNA, Fungal; D012333:RNA, Messenger; D012334:RNA, Neoplasm; D014157:Transcription Factors