An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification Using Support Vector Machine
Loading...
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Walailak University - Walailak Journal of Science and Technology
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