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

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