A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels

Adebiyi, Marion and Arowolo, Michael Olaolu and Olugbara, Oludayo (2021) A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels. Bulletin of Electrical Engineering and Informatics, 10 (2). pp. 1071-1079. ISSN 2089-3191

Text BEE Buletin paper-Prof-Olu-Marion Published (2021_06_27 14_50_26 UTC).pdf - Published Version Download (363kB)

Abstract

Malaria larvae embrace unpredictable variable life periods as they spread across many stratospheres of the mosquito vectors. There are transcriptomes of a thousand distinct species. Ribonucleic acid sequencing (RNA-seq) is a ubiquitous gene expression strategy that contributes to the improvement of genetic survey recognition. RNA-seq measures gene expression transcripts data, including methodological enhancements to machine learning procedures. Scientists have suggested many addressed learning for the study of biological evidence. An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification using SVM-Kernel algorithms. The efficacy of this assay is tested, and the outcome of the experiment obtained an accuracy metric of 93% and 96% respectively.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User:	Mr Uchechukwu F. Ekpendu
Date Deposited:	07 Jul 2021 11:04
Last Modified:	07 Jul 2021 11:04
URI:	https://eprints.lmu.edu.ng/id/eprint/3232

Actions (login required)

View Item