title: Prioritizing predicted cis-regulatory elements for co-expressed gene sets based on Lasso regression models.
abstract: Computational prediction of cis-regulatory elements for a set of co-expressed genes based on sequence analysis provides an overwhelming volume of potential transcription factor binding sites. It presents a challenge to prioritize transcription factors for regulatory functional studies. A novel approach based on the use of Lasso regression models is proposed to address this problem. We examine the ability of the Lasso model using time-course microarray data obtained from a comprehensive study of gene expression profiles in skin and mucosal wounds in mouse over all stages of wound healing.
authors.affiliation: Department of Bioengineering (M/C 063), University of Illinois at Chicago, 851 S Morgan St, SEO 218, Chicago, IL 60607, USA. hhu4@uic.edu
authors.forename: Hong
authors.initials: H
authors.lastname: Hu
authors.forename: Damian
authors.initials: D
authors.lastname: Roqueiro
authors.forename: Yang
authors.initials: Y
authors.lastname: Dai
mesh_terms: D000818:Animals; D001665:Binding Sites; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D009092:Mucous Membrane / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011401:Promoter Regions, Genetic; D012044:Regression Analysis; D012867:Skin / Q000378:metabolism; D014157:Transcription Factors / Q000737:chemistry* / Q000378:metabolism
chemical_list: D014157:Transcription Factors