This article is part of the supplement: Eleventh International Conference on Bioinformatics (InCoB2012): Bioinformatics

Open Access Proceedings

A probabilistic coevolutionary biclustering algorithm for discovering coherent patterns in gene expression dataset

Je-Gun Joung1,2,3, Soo-Jin Kim4, Soo-Yong Shin5,6 and Byoung-Tak Zhang4,7*

Author Affiliations

1 Seoul National University Biomedical Informatics (SNUBI), Seoul 110-799, Korea

2 Systems Biomedical Informatics National Core Research Center, Seoul 110-799, Korea

3 Institute of Endemic Diseases, Seoul National University College of Medicine, Seoul 110-799, Korea

4 Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea

5 Department of Clinical Epidemiology & Biostatistics, Asan Medical Center, Seoul, 138-736, Korea

6 University of Ulsan College of Medicine, Seoul, 138-736, Korea

7 School of Computer Science and Engineering, Seoul National University, Seoul, 151-744, Korea

For all author emails, please log on.

BMC Bioinformatics 2012, 13(Suppl 17):S12 doi:10.1186/1471-2105-13-S17-S12

Published: 13 December 2012

Abstract

Background

Biclustering has been utilized to find functionally important patterns in biological problem. Here a bicluster is a submatrix that consists of a subset of rows and a subset of columns in a matrix, and contains homogeneous patterns. The problem of finding biclusters is still challengeable due to computational complex trying to capture patterns from two-dimensional features.

Results

We propose a Probabilistic COevolutionary Biclustering Algorithm (PCOBA) that can cluster the rows and columns in a matrix simultaneously by utilizing a dynamic adaptation of multiple species and adopting probabilistic learning. In biclustering problems, a coevolutionary search is suitable since it can optimize interdependent subcomponents formed of rows and columns. Furthermore, acquiring statistical information on two populations using probabilistic learning can improve the ability of search towards the optimum value. We evaluated the performance of PCOBA on synthetic dataset and yeast expression profiles. The results demonstrated that PCOBA outperformed previous evolutionary computation methods as well as other biclustering methods.

Conclusions

Our approach for searching particular biological patterns could be valuable for systematically understanding functional relationships between genes and other biological components at a genome-wide level.