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

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dc.contributor.authorArowolo, Micheal Olaolu
dc.contributor.authorAwotunde, Joseph Bamidele
dc.contributor.authorAyegba, Peace
dc.contributor.authorHaroon-Sulyman, Shakirat Oluwatosin
dc.date.accessioned2023-10-03T10:38:36Z
dc.date.available2023-10-03T10:38:36Z
dc.date.issued2022-11-14
dc.description.abstractRecent 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.en_US
dc.identifier.citationArowolo, 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.en_US
dc.identifier.issn1746-6172
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11799
dc.language.isoenen_US
dc.publisherInderscience Publishers (IEL)en_US
dc.subjectmalaria vectoren_US
dc.subjectgene expressionen_US
dc.subjectanalysis of varianceen_US
dc.subjectANOVAen_US
dc.subjectant colony optimisationen_US
dc.subjectdata classificationen_US
dc.subjectsupport vector machineen_US
dc.subjectmachine learningen_US
dc.subjecthigh-dimensional dataen_US
dc.subjectdata analysisen_US
dc.subjectvector-borne diseaseen_US
dc.subjectmulti-layer perceptionen_US
dc.titleRelevant gene selection using ANOVA-ant colony optimisation approach for malaria vector data classificationen_US
dc.typeArticleen_US

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