Title: Relevant gene selection using ANOVA-ant colony optimisation approach for malaria vector data classification

Authors: Micheal Olaolu Arowolo; Joseph Bamidele Awotunde; Peace Ayegba; Shakirat Oluwatosin Haroon-Sulyman

Addresses: Department of Computer Science, College of Pure and Applied Sciences, Landmark University Omu-Aran, Kwara State, Nigeria ' Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin, Kwara State, Nigeria ' Department of Computer Science, College of Pure and Applied Sciences, Landmark University Omu-Aran, Kwara State, Nigeria ' Department of Information and Communication Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin, Kwara State, Nigeria

Abstract: Recent progress in gene expression data research makes it possible to quantify and identify several thousand genes' expressions simultaneously. For malaria infection and transmission, gene expression data classification using dimensionality reduction is a standard approach in gene expression data analysis and proposed for this study. A major problem occurs in the reduction of high dimensional data, it plays a significant role in improving the precision of classification, allowing biologists and clinicians to correctly predict infections in humans by choosing a limited subclass of appropriate genes and deleting redundant, and noisy genes. The combination of a novel analysis of variance (ANOVA) with ant colony optimisation (ACO) approach as a hybrid feature selection to select relevant genes is suggested in this study to minimise the redundancy between genes, and SVM is used for classification. The proposed method's efficacy was shown by the experimental outcomes based on the high-dimensional of gene expression data.

Keywords: malaria vector; gene expression; analysis of variance; ANOVA; ant colony optimisation; data classification; support vector machine; machine learning; high-dimensional data; data analysis; vector-borne disease; multi-layer perception.

DOI: 10.1504/IJMIC.2022.10052108

International Journal of Modelling, Identification and Control, 2022 Vol.41 No.1/2, pp.12 - 21

Received: 08 Apr 2021
Accepted: 05 Sep 2021

Published online: 22 Nov 2022 *

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