By Topic

Disease gene-fishing in molecular interaction networks: A case study in colorectal 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

3 Author(s)
Hui Huang ; Sch. of Inf., Indiana Univ., Indianapolis, IN, USA ; Jiao Li ; Chen, J.Y.

In the post-genome era, disease-relevant gene finding and prioritization have focused on genome-wide association studies and molecular interaction networks, due to their power in characterizing the functions of genes/proteins in genomics and network biology contexts. In this paper, we describe a simple yet generic computational framework based on protein interaction networks to perform and evaluate disease gene-hunting, using colorectal cancer as a case study. We applied statistical measurements including specificity, sensitivity and Positive Predictive Value (PPV) to evaluate the performance of disease gene ranking methods, which we broke down into seed gene selection, protein interaction data quality and coverage, and network-based gene-ranking strategies. We discovered that best results may be obtained by using curated gene sets as seeds, applying protein interaction data set with high data coverage and decent quality, and adopting variants of local degree methods.

Published in:

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Date of Conference:

3-6 Sept. 2009