Biclustering analysis of gene expression data using evolutionary algorithms

Abstract

Bicluster analysis has emerged as a powerful tool for unsupervised pattern discovery, especially for the analysis of gene expression data. This chapter discusses the biclustering problem, the different bicluster patterns, existing biclustering techniques, and how evolutionary algorithms (EAs) have been applied to solve the biclustering problem. Gene ontology (GO), metabolic pathway maps (MPMs), and protein-protein interaction (PPI) networks can be used to determine the biological functional relevance of genes and conditions in a bicluster. Many biclustering algorithms have been proposed recently and they can be grouped into several categories depending on the bicluster model, the search strategy, and the algorithmic framework used. Factorization-based biclustering algorithm uses spectral decomposition technique to uncover natural substructures that are related to the main patterns of the data matrix. There are two main EA frameworks. The first is based on the genetic algorithm (GA), and the second is based on the artificial immune system (AIS).