Correlation–based scatter search for discovering biclusters from gene expression data

Authors:
Juan A. Nepomuceno;Alicia Troncoso;Jesús S. Aguilar–Ruiz
Affiliations:
Department of Computer Science, University of Sevilla, Spain;Area of Computer Science, Pablo de Olavide University of Sevilla, Spain;Area of Computer Science, Pablo de Olavide University of Sevilla, Spain
Venue:
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2010

Citing 8
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Abstract

Scatter Search is an evolutionary method that combines existing solutions to create new offspring as the well–known genetic algorithms. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. However, biclusters with certain patterns are more interesting from a biological point of view. Therefore, the proposed Scatter Search uses a measure based on linear correlations among genes to evaluate the quality of biclusters. As it is usual in Scatter Search methodology an improvement method is included which avoids to find biclusters with negatively correlated genes. Experimental results from yeast cell cycle and human B-cell lymphoma datasets are reported showing a remarkable performance of the proposed method and measure.