An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification Using Support Vector Machine
| dc.contributor.author | Arowolo, M.O. | |
| dc.contributor.author | Adebiyi, M.O. | |
| dc.contributor.author | Nnodim, C.T. | |
| dc.contributor.author | Abdulsalam, S.O. | |
| dc.contributor.author | Adebiyi, A.A. | |
| dc.date.accessioned | 2025-10-29T11:30:02Z | |
| dc.date.available | 2025-10-29T11:30:02Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively. Keywords: RNA-seq, Adaptive genetic algorithm, Recursive feature elimination, Malaria vector, Support Vector Machine kernels | |
| dc.identifier.uri | https://kwasuspace.kwasu.edu.ng/handle/123456789/6227 | |
| dc.language.iso | en | |
| dc.publisher | Walailak University - Walailak Journal of Science and Technology | |
| dc.title | An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification Using Support Vector Machine | |
| dc.type | Article |