On Evolutionary Algorithms for Biclustering of Gene Expression Data

Title:On Evolutionary Algorithms for Biclustering of Gene Expression Data

Volume: 10 Issue: 3

Author(s): A. Carballido Jessica, A. Gallo Cristian, S. Dussaut Julieta and Ponzoni Ignacio

Affiliation:

Keywords: Biclustering, evaluation metrics, evolutionary algorithms, gene expression data, microarray analysis, regulatory networks.

Abstract: Past decades have seen the rapid development of microarray technologies making available large amounts of gene expression data. Hence, it has become increasingly important to have reliable methods to interpret this information in order to discover new biological knowledge. In this review paper we aim to describe the main existing evolutionary methods that analyze microarray gene expression data by means of biclustering techniques. Strategies will be classified according to the evaluation metric used to quantify the quality of the biclusters. In this context, the main evaluation measures, namely mean squared residue, virtual error and transposed virtual error, are first presented. Then, the main evolutionary algorithms, which find biclusters in gene expression data matrices using those metrics, are described and compared.

Cite this article as:

Jessica Carballido A., Cristian Gallo A., Julieta Dussaut S. and Ignacio Ponzoni, On Evolutionary Algorithms for Biclustering of Gene Expression Data, Current Bioinformatics 2015; 10 (3) . https://dx.doi.org/10.2174/1574893609666140829204739