By Topic

Predicting drug efficacy based on the integrated breast cancer pathway model

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

5 Author(s)
Hui Huang ; School of Informatics, Indiana University, USA ; Xiaogang Wu ; Sara Ibrahim ; Marianne McKenzie
more authors

This study is based on a simple hypothesis - “ideal” drugs for a patient can cure the patient's disease by modulating the gene expression profile of the patient to a similar level with those in healthy people, on the pathway level. To verify this hypothesis, we present a computational framework to evaluate drug effects on gene expression profiles in breast cancer. First, a breast cancer pathway model has been constructed by utilizing a computational connectivity maps (C-Maps) approach. This model includes important protein and drug information. In this pathway, specific drug-protein interactions (i.e. activation/inhibition) are annotated as edge attributes. Thus, we get a novel Pharmacology Effect Network, or PEN. We then develop a ranking algorithm called PET (i.e. Pharmacological Effect on Target) to combine gene expression information and our constructed PEN to evaluate specific drugs' efficacies. Finally, we applied PET and PEN to evaluate 23 breast cancer drugs. The ranking results clearly show the validity of our framework.

Published in:

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

Date of Conference:

4-6 Dec. 2011