Sign up & Download
Sign in

Noise-robust algorithm for identifying functionally associated biclusters from gene expression data

by Jaegyoon Ahn, Youngmi Yoon, Sanghyun Park
Information Sciences ()

Abstract

Biclusters are subsets of genes that exhibit similar behavior over a set of conditions. A biclustering algorithm is a useful tool for uncovering groups of genes involved in the same cellular processes and groups of conditions under which these processes take place. In this paper, we propose a polynomial time algorithm to identify functionally highly correlated biclusters. Our algorithm identifies (1) gene sets that simultaneously exhibit additive, multiplicative, and combined patterns and allow high levels of noise, (2) multiple, possibly overlapped, and diverse gene sets, (3) biclusters that simultaneously exhibit negatively and positively correlated gene sets, and (4) gene sets for which the functional association is very high. We validate the level of functional association in our method by using the GO database, protein-protein interactions and KEGG pathways. © 2010 Elsevier Inc. All rights reserved.

Cite this document (BETA)

Readership Statistics

4 Readers on Mendeley
by Discipline
 
 
 
by Academic Status
 
75% Ph.D. Student
 
25% Librarian

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Already have an account? Sign in