By Topic

AP-Based Consensus Clustering for Gene Expression Time Series

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)
Tai-Yu Chiu ; Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan ; Ting-Chieh Hsu ; Jia-Shung Wang

We propose an unsupervised approach for analyzing gene time-series datasets. Our method combines Affinity Propagation (AP) and the spirit of consensus clustering-- extracting multiple partitions from different time intervals. Without priori knowledge of total number of clusters and exemplars, this method holds the relationship between genes through different time intervals, and eliminates the influence from noises and outliers. We demonstrate our method with both synthetic and real gene expression datasets showing significant improvement in accuracy and efficiency.

Published in:

Pattern Recognition (ICPR), 2010 20th International Conference on

Date of Conference:

23-26 Aug. 2010