Parallel biclustering detection using strength Pareto front evolutionary algorithm

Golchin, Maryam, and Liew, Alan Wee Chung (2017) Parallel biclustering detection using strength Pareto front evolutionary algorithm. Information Sciences, 415-416. pp. 283-297.

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Biclustering has become a popular technique to analyse gene expression datasets and extract valuable information by clustering rows and columns of a dataset simultaneously. Using a good merit function together with a suitable local search can lead to the detection of interesting biclusters. In this paper, a multi-objective evolutionary algorithm with local search is proposed to search for multiple biclusters concurrently in a single run of the evolutionary algorithm. We call our method PBD-SPEA2 (Parallel Biclustering Detection using Strength Pareto front Evolutionary Algorithm 2). In our algorithm, a new dynamic encoding scheme is used to encode multiple biclusters in each individual. Our multi-objective function consists of three objectives that simultaneously optimizes the homogeneity of the elements in the bicluster, the size of the bicluster, and the variance of the column in the bicluster with respect to the entire dataset. Crossover is done by selecting and combining the best biclusters among the encoded biclusters from both parents through a strategy of exploration and exploitation. Finally, a sequential selection procedure is used to select the final set of biclusters from individuals that constitute the Pareto front. Experimental results are presented to compare the performance and biological enrichment of detected biclusters with several existing algorithms.

Item ID: 76976
Item Type: Article (Research - C1)
ISSN: 1872-6291
Copyright Information: © 2017 Elsevier Inc. All rights reserved.
Date Deposited: 07 Dec 2022 01:33
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4613 Theory of computation > 461305 Data structures and algorithms @ 100%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 100%
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