Relevant gene selection using ANOVA-ant colony optimisation approach for malaria vector data classification
No Thumbnail Available
Date
2022-11-14
Journal Title
Journal ISSN
Volume Title
Publisher
Inderscience Publishers (IEL)
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.
Description
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
Citation
Arowolo, M. O., Awotunde, J. B., Ayegba, P., & Haroon-Sulyman, S. O. (2022). Relevant gene selection using ANOVA-ant colony optimisation approach for malaria vector data classification. International Journal of Modelling, Identification and Control, 41(1-2), 12-21.