Group SCAD regression analysis for microarray time course gene expression data | Zendy

Lifeng Wang | Zendy; Guang Chen | Zendy; Hongzhe Li | Zendy

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Group SCAD regression analysis for microarray time course gene expression data

Author(s) -

Lifeng Wang,

Guang Chen,

Hongzhe Li

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm125

Subject(s) - microarray analysis techniques , scad , gene , computational biology , gene expression , linear regression , regression analysis , gene regulatory network , biology , regression , gene expression profiling , genetics , computer science , mathematics , statistics , machine learning , psychology , psychiatry , myocardial infarction

Since many important biological systems or processes are dynamic systems, it is important to study the gene expression patterns over time in a genomic scale in order to capture the dynamic behavior of gene expression. Microarray technologies have made it possible to measure the gene expression levels of essentially all the genes during a given biological process. In order to determine the transcriptional factors (TFs) involved in gene regulation during a given biological process, we propose to develop a functional response model with varying coefficients in order to model the transcriptional effects on gene expression levels and to develop a group smoothly clipped absolute deviation (SCAD) regression procedure for selecting the TFs with varying coefficients that are involved in gene regulation during a biological process.

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