A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

Simple item page
dc.contributor.authorArowolo, Micheal Olaolu
dc.contributor.authorAdebiyi, Marion Olubunmi
dc.contributor.authorAdebiyi, Ayodele Ariyo
dc.date.accessioned2023-08-31T14:40:05Z
dc.date.available2023-08-31T14:40:05Z
dc.date.issued2020-09-24
dc.description.abstractMalaria 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 respectivelyen_US
dc.description.sponsorshipACE: Applied Informatics and Communicationen_US
dc.identifier.issn1693-6930
dc.identifier.urihttp://hdl.handle.net/123456789/2109
dc.language.isoenen_US
dc.publisherTELKOMNIKA Telecommunication, Computing, Electronics and Controlen_US
dc.relation.ispartofseriesTELKOMNIKA Telecommunication, Computing, Electronics and Control;Vol. 19, No. 1, February 2021
dc.subjectDecision treeen_US
dc.subjectGenetic algorithmen_US
dc.subjectKNNen_US
dc.subjectMosquito anophelesen_US
dc.subjectRibonucleic acid sequencingen_US
dc.subjectACE: Applied Informatics and Communicationen_US
dc.subjectNigeriaen_US
dc.subjectCovenant Universityen_US
dc.subjectDigital Developmenten_US
dc.titleA genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision treeen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
KNN.pdf
Size:
495 KB
Format:
Adobe Portable Document Format
Description:
Main article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
STEM