Arowolo, Micheal OlaoluAdebiyi, Marion OlubunmiAdebiyi, Ayodele Ariyo2023-08-312023-08-312020-09-241693-6930http://hdl.handle.net/123456789/2109Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNAseq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectivelyenDecision treeGenetic algorithmKNNMosquito anophelesRibonucleic acid sequencingACE: Applied Informatics and CommunicationNigeriaCovenant UniversityDigital DevelopmentArticle