title: Dynamic biclustering of microarray data by multi-objective immune optimization.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence.
authors: School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China. ljwnudt@163.com
authors: Junwan
authors: J
authors: Liu
authors: Zhoujun
authors: Z
authors: Li
authors: Xiaohua
authors: X
authors: Hu
authors: Yiming
authors: Y
authors: Chen
authors: E K
authors: EK
authors: Park
mesh_terms: D000465:Algorithms; D001402:B-Lymphocytes / Q000166:cytology* / Q000276:immunology; D002453:Cell Cycle; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D016681:Genome, Fungal; D015894:Genome, Human; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics*; D013997:Time Factors