By Topic

Connecting clusters of patient to drug responses of cell lines to suggest personalized therapeutics for breast cancer

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)
Gordonov, S. ; Dept. of Pharmacology & Syst. Therapeutics, Mount Sinai Sch. of Med., New York, NY, USA ; Ma'ayan, A.

Recent surge in genome-wide expression data from patient tumors and cell-lines in breast cancer, as well as response data of breast cancer cell-lines to many drugs, opens the opportunity for data integration approaches that can lead to better personalized therapeutics. Here we integrated such data to generate a tripartite network that connects clusters of patients to cell-lines, and cell-lines to drugs to suggest which drugs may work best for each cluster of patients. We combined gene expression profiles from 400 patient tumor samples from two independent publicly-available studies, with gene expression and drug growth-inhibition-response profiles of 31 breast cancer cell-lines to build this tripartite network.

Published in:

Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on

Date of Conference:

4-7 Oct. 2012