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

Jaegyoon Ahn, Youngmi Yoon, Sanghyun Park

Research output: Contribution to journalArticle

13 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)435-449
Number of pages15
JournalInformation sciences
Volume181
Issue number3
DOIs
Publication statusPublished - 2011 Feb 1

Fingerprint

Robust Algorithm
Gene Expression Data
Gene expression
Genes
Gene
Proteins
Biclustering
Protein-protein Interaction
Polynomial-time Algorithm
Pathway
Multiplicative
Polynomials
Subset

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Cite this

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Noise-robust algorithm for identifying functionally associated biclusters from gene expression data. / Ahn, Jaegyoon; Yoon, Youngmi; Park, Sanghyun.

In: Information sciences, Vol. 181, No. 3, 01.02.2011, p. 435-449.

Research output: Contribution to journalArticle

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