Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
BiHEA: A Hybrid Evolutionary Approach for Microarray Biclustering
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
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In this paper, a new biclustering analysis toolbox called BAT, which is based on the BiHEA (Biclustering via a Hybrid Evolutionary Algorithm), is presented. The BiHEA is a memetic approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with a local search technique in order to perform microarray biclustering. This method simultaneously considers several goals for optimization, giving as a result a set of biclusters that present a satisfactory trade-off between all of them. The novel software introduced in this article provides the possibility of running the BiHEA along with several pre-processing facilities for the input data and different visualization and statistical tools for the analysis of the biclusters.