Kwara State University
Sulaiman Abdulsalam
Designation: Lecturer I
Department: Computer Science
| My Publications | ||||||||
|---|---|---|---|---|---|---|---|---|
| S/N | Title | Abstract | Authors | Volume Numbers | Publication Type | Publication Date | Link | |
| 1 | An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification Using Support Vector Machine |
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 | Arowolo M.O., Adebiyi M.O., Nnodim C.T., Abdulsalam S.O. and Adebiyi A.A. | 18(17) | International-based Publication | 2021-08-26 | https://wjst.wu.ac.th/index.php/wjst/article/view/9849 | |