A gene selection algorithm for microarray cancer classification using an improved particle swarm optimization.

Autor: Nagra AA; Faculty of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan., Khan AH; Faculty of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan., Abubakar M; Faculty of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan. abubakarqazi@lgu.edu.pk., Faheem M; School of Technology and Innovations, University of Vaasa, Vaasa, Finland., Rasool A; Department of Computer, Bakhtar University Kabul, Kabul, Afghanistan. adilrasool@bakhtar.edu.af., Masood K; Faculty of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan., Hussain M; Department of Computer Science, University of Central Punjab Pakistan, Lahore, 54000, Pakistan.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Aug 23; Vol. 14 (1), pp. 19613. Date of Electronic Publication: 2024 Aug 23.
DOI: 10.1038/s41598-024-68744-6
Abstrakt: Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (deoxyribonucleic acid microarray] facilitates in computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This novel algorithm establishes a balance between the exploitation and exploration capabilities of the improved inertia weight adaptive particle swarm optimization. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) algorithm is employed for solution explorations. Each particle in the SIW-APSO increases its position and velocity iteratively through an evolutionary process. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been employed to identify several genes in the cancer dataset. The classification algorithm contains ELM, K-centroid nearest neighbor, and support vector machine to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.
(© 2024. The Author(s).)
Databáze: MEDLINE
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