A robust gene clustering algorithm based on clonal selection in multiobjective optimization framework

• Published in: Expert systems with applications Vol. 113; pp. 301 - 314
• Main Authors: Zareizadeh, Zahra, Helfroush, Mohammad Sadegh, Rahideh, Akbar, Kazemi, Kamran
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2018; Elsevier BV
• Subjects: Algorithms; Clonal selection; Clustering; Datasets; Gene clustering; Gene expression; Genes; Genomics; Microarray; Multi-objective optimization; Multiple objective analysis; Optimization; Optimization algorithms; Clonal selection; Multi-objective optimization; Microarray; Gene clustering
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2018.06.047

•Integration of clustering validity indexes as two conflicting objective functions.•Fast convergence because of a novel population updating mechanism.•Possibility of varying the number of clusters in a predefined range.•Fixed population members length in spite of variable cluster numbers. Gene clustering is a prerequisite in the analysis of microarray data where sets of co-expressed genes are clustered. In this paper, a multi-objective clonal selection optimization algorithm (MCSOA) is developed based on the immune system behavior for gene clustering purposes in which the number of clusters can vary in a predefined range. To achieve a reliable clustering outcome on various gene expression (GE) datasets, the most effective clustering validity indexes are incorporated and represented in terms of two conflicting objective functions. For the sake of fast convergence to the optimal solutions, a new population updating mechanism is iteratively applied to select the less-dominate solutions of the previous iteration. The proposed clustering technique is implemented on various publicly available microarray datasets. Comparing the results with those of the widely used gene clustering techniques confirms the superiority and efficacy of the proposed technique.