By Topic

Incorporating Gene Functions into Regression Analysis of DNA-Protein Binding Data and Gene Expression Data to Construct Transcriptional Networks

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
$31 $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

2 Author(s)
Peng Wei ; Sch. of Public Health, Minnesota Univ., Minneapolis, MN ; Wei Pan

Useful information on transcriptional networks has been extracted by regression analyses of gene expression data and DNA-protein binding data. However, a potential limitation of these approaches is their assumption on the common and constant activity level of a transcription factor (TF) on all of the genes in any given experimental condition, for example, any TF is assumed to be either an activator or a repressor, but not both, whereas it is known that some TFs can be dual regulators. Rather than assuming a common linear regression model for all of the genes, we propose using separate regression models for various gene groups; the genes can be grouped based on their functions or some clustering results. Furthermore, to take advantage of the hierarchical structure of many existing gene function annotation systems such as gene ontology (GO), we propose a shrinkage method that borrows information from relevant gene groups. Applications to a yeast data set and simulations lend support to our proposed methods. In particular, we find that the shrinkage method consistently works well under various scenarios. We recommend the use of the shrinkage method as a useful alternative to the existing methods.

Published in:

Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:5 ,  Issue: 3 )