A multi-objective approach to discover biclusters in microarray data

The main motivation for using a multi–objective evolutionary algorithm for finding biclusters in gene expression data is motivated by the fact that when looking for biclusters in gene expression matrix, several objectives have to be optimized simultaneously, and often these objectives are in conflict with each other. Moreover, the use of evolutionary computation is justified by the huge dimensionality of the search space, since it is known that evolutionary algorithms have great exploration power. We focus our attention on finding biclusters of high quality with large variation. This is because, in expression data analysis, the most important goal may not be finding biclusters containing many genes and conditions, as it might be more interesting to find a set of genes showing similar behavior under a set of conditions. Experimental results confirm the validity of the proposed technique. Categories and Subject Descriptors I.5.3 [Pattern Recognition]: Clustering; J.3 [Life and ...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Federico Divina, Jesús S. Aguilar-Ruiz
• Keywords: Expression Data, GECCO 2007, Gene Expression, Gene Expression Data