Evolutionary biclustering algorithms: an experimental study on microarray data

• Published in: Soft computing (Berlin, Germany) Vol. 23; no. 17; pp. 7671 - 7697
• Main Authors: Maâtouk, Ons, Ayadi, Wassim, Bouziri, Hend, Duval, Béatrice
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-09-2019; Springer Nature B.V; Springer Verlag
• Subjects: Artificial Intelligence; Bioinformatics; Biology; Clustering; Computational Intelligence; Computer Science; Control; Data mining; Engineering; Evolutionary algorithms; Gene expression; Genetic algorithms; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Robotics; Biclustering; Data mining; Evolutionary algorithm; Microarray data; Genetic operators
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-018-3394-4

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.