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The extraction of knowledge from large biological data is among the main challenges of bioinformatics. Several data mining techniques have been proposed to extract data; in this work, we focus on biclustering which has grown considerably in recent years. Biclustering aims to extract a set of genes with similar behavior under a condition set. In this paper, we propose an evolutionary biclustering algorithm and we analyze its performance by varying its genetic components. Hence, several versions of the evolutionary biclustering algorithm are introduced. Further, an experimental study is achieved on two real microarray datasets and the results are compared to other state-of-the-art biclustering algorithms. This thorough study allows to retain the best combination of operators among the various experienced choices. [ABSTRACT FROM AUTHOR]

Biclustering is an unsupervised classification technique that plays an increasingly important role in the study of modern biology. This data mining technique has provided answers to several challenges raised by the analysis of biological data and more particularly the analysis of gene expression data. It aims to cluster simultaneously genes and conditions. These unsupervised techniques are based essentially on the assumption that the extraction of the co-expressed genes allows to have co-regulated genes. In addition, the integration of biological information in the search process may induce to the extraction of relevant and non-trivial biclusters. Therefore, this work proposes an evolutionary algorithm based on local search method that relies on biological knowledge. An experimental study is achieved on real microarray datasets to evaluate the performance of the proposed algorithm. The assessment and the comparison are based on statistical and biological criteria. A cross-validation experiment is also used to estimate its accuracy. Promising results are obtained. They demonstrate the importance of the integration of the biological knowledge in the biclustering process to foster the efficiency and to promote the discovery of non-trivial and biologically relevant biclusters. • An EA is proposed for gene expression biclustering, integrating biological knowledge. • A new way to reflect the statistical and biological criteria is introduced. • Both criteria types are considered separately and defined by its own function. • The bicluster combination relies on biological knowledge to ensure the biological relevance. • The mutation method helps to annotate genes with unknown biological functionalities. [ABSTRACT FROM AUTHOR]