A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree
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Date
2020-09-24
Journal Title
Journal ISSN
Volume Title
Publisher
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Abstract
Malaria 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 respectively
Description
Keywords
Decision tree, Genetic algorithm, KNN, Mosquito anopheles, Ribonucleic acid sequencing, ACE: Applied Informatics and Communication, Nigeria, Covenant University, Digital Development