标题：An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels
期刊名称：Walailak Journal of Science and Technology (WJST)
印刷版ISSN：2228-835X
出版年度：2021
卷号：18
期号：17
页码：1-11
DOI：10.48048/wjst.2021.9849
语种：English
出版社：Institute of Research and Development, Walailak University.
摘要：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.HIGHLIGHTSDimensionality reduction method based of feature selectionClassification using Support vector machineClassification of malaria vector dataset using an adaptive GA-RFE-SVMGRAPHICAL.
