By Topic

Prioritizing predicted cis-regulatory elements for co-expressed gene sets based on Lasso regression models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Hong Hu ; Department of Bioengineering (M/C 063), University of Illinois at Chicago, 851 S Morgan St, SEO 218, Chicago, IL 60607 USA ; Damian Roqueiro ; Yang Dai

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.

Published in:

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

Aug. 30 2011-Sept. 3 2011