This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2010
Dynamic biclustering of microarray data by multi-objective immune optimization
1 School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
2 State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
3 Beijing Key Laboratory of Network Technology, BeiHang University, Beijing 100191, China
4 College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA
5 School of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China
6 CSI-CUNY in Staten Island, NY, USA
BMC Genomics 2011, 12(Suppl 2):S11 doi:10.1186/1471-2164-12-S2-S11Published: 27 July 2011
Newly microarray technologies yield large-scale datasets. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. Systematic analysis of those datasets provides the increasing amount of information, which is urgently needed in the post-genomic era. Biclustering, which is a technique developed to allow simultaneous clustering of rows and columns of a dataset, might be useful to extract more accurate information from those datasets. Biclustering requires the optimization of two conflicting objectives (residue and volume), and a multi-objective artificial immune system capable of performing a multi-population search. As a heuristic search technique, artificial immune systems (AISs) can be considered a new computational paradigm inspired by the immunological system of vertebrates and designed to solve a wide range of optimization problems. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective optimization model is suitable for solving biclustering problem.
Based on dynamic population, this paper proposes a novel dynamic multi-objective immune optimization biclustering (DMOIOB) algorithm to mine coherent patterns from microarray data. Experimental results on two common and public datasets of gene expression profiles show that our approach can effectively find significant localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner.
The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence.