By Topic

New approaches to clustering microarray time-series data using multiple expression profile alignment

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

4 Author(s)
Numanul Subhani ; School of Computer Science, 5115 Lambton Tower, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, N9B 3P4, Canada ; Luis Rueda ; Alioune Ngom ; Conrad Burden

An important process in functional genomic studies is clustering microarray time-series data, where genes with similar expression profiles are expected to be functionally related. Clustering microarray time-series data via pairwise alignment of piecewise linear profiles has been recently introduced. In this paper, we propose a clustering approach based on a multiple profile alignment of natural cubic spline and piecewise linear representations of gene expression profiles. We combine these multiple alignment approaches with k-means. We ran our methods on a well-known data set of pre-clustered Saccharomyces cerevisiae gene expression profiles and a data set of 3315 Pseudomonas aeruginosa expression profiles. We assessed the validity of the resulting clusters and applied a c-nearest neighbor classifier for evaluating the performance of our approaches, obtaining accuracies of 89.51% and 86.12% respectively, on Saccharomyces cerevisiae data, and 90.90% and 93.71% accuracies for cubic spline and piecewise linear respectively on Pseudomonas aeruginosa data.

Published in:

Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2010 IEEE Symposium on

Date of Conference:

2-5 May 2010