title: Quality measures for gene expression biclusters.
abstract: An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression pattern, is one of the main objectives. Due to the problem complexity, heuristic searches are usually used instead of exhaustive algorithms. Furthermore, most of biclustering approaches use a measure or cost function that determines the quality of biclusters. Having a suitable quality metric for bicluster is a critical aspect, not only for guiding the search, but also for establishing a comparison criteria among the results obtained by different biclustering techniques. In this paper, we analyse a large number of existing approaches to quality measures for gene expression biclusters, as well as we present a comparative study of them based on their capability to recognize different expression patterns in biclusters.
authors.affiliation: Department of Computer Languages, University of Seville, Seville, Spain.
authors.forename: Beatriz
authors.initials: B
authors.lastname: Pontes
authors.affiliation: School of Engineering, Pablo de Olavide University, Seville, Spain.
authors.forename: Ral
authors.initials: R
authors.lastname: Girldez
authors.affiliation: School of Engineering, Pablo de Olavide University, Seville, Spain.
authors.forename: Jess S
authors.initials: JS
authors.lastname: Aguilar-Ruiz
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D015870:Gene Expression; D006801:Humans; D008957:Models, Genetic