By Topic

Feature Cluster Selection for High-Throughput Data Analysis

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

2 Author(s)
Lei Yu ; Binghamton Univ., Binghamton ; Hao Li

Although feature selection has proven effective in sample class prediction, it is not adequate for identifying leads for potentially useful biomarkers by high-throughput biological data analysis. The large number of equally good predictive sets and the disparity among them reveals the gap between feature selection and biomarker identification. We propose to bridge this gap by a new data mining task, feature cluster selection, which aims to select and group all relevant features in a data set into a small number of coherent clusters. We provide both theoretical framework and empirical formulation for the new problem, and propose the 3M algorithm. Experiments on microarray data show that the algorithm can select highly predictive representative gene sets and discover gene clusters with statistical significance.

Published in:

Bioinformatics and Biomedicine, 2007. BIBM 2007. IEEE International Conference on

Date of Conference:

2-4 Nov. 2007