title: Biclustering on expression data: A review.
abstract: Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.
authors.affiliation: Department of Languages and Computer Systems, University of Seville, Seville, Spain. Electronic address: bepontes@us.es.
authors.forename: Beatriz
authors.initials: B
authors.lastname: Pontes
authors.affiliation: School of Engineering, Pablo de Olavide University, Seville, Spain. Electronic address: giraldez@upo.es.
authors.forename: Raúl
authors.initials: R
authors.lastname: Giráldez
authors.affiliation: School of Engineering, Pablo de Olavide University, Seville, Spain. Electronic address: aguilar@upo.es.
authors.forename: Jesús S
authors.initials: JS
authors.lastname: Aguilar-Ruiz
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis
keywords: Biclustering techniques; Gene expression data; Microarray analysis