Predicting RNA-Seq data using genetic algorithm and ensemble classification algorithms

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dc.contributor.authorArowolo, Micheal Olaolu
dc.contributor.authorAdebiyi, Marion O.
dc.contributor.authorAdebiyi, Ayodele A.
dc.date.accessioned2023-08-31T14:56:04Z
dc.date.available2023-08-31T14:56:04Z
dc.date.issued2020-05-03
dc.description.abstractMalaria parasites accept uncertain, inconsistent life span breeding through vectors of mosquitoes stratospheres. Thousands of different transcriptome parasites exist. A prevalent Ribonucleic acid sequencing (RNA-seq) technique for gene expression has brought about enhanced identifications of genetical queries. Computation of RNA-seq gene expression data transcripts requires enhancements using analytical machine learning procedures. Numerous learning approaches have been adopted for analyzing and enhancing the performance of biological data and machines. In this study, a Genetic algorithm dimensionality reduction technique is proposed to fetch relevant information from a huge dimensional RNA-seq dataset, and classification uses Ensemble classification algorithms. The experiment is performed using a mosquito Anopheles gambiae dataset with a classification accuracy of 81.7% and 88.3%en_US
dc.description.sponsorshipACE: Applied Informatics and Communicationen_US
dc.identifier.issn2502-4752
dc.identifier.urihttp://hdl.handle.net/123456789/2110
dc.language.isoenen_US
dc.publisherIndonesian Journal of Electrical Engineering and Computer Scienceen_US
dc.relation.ispartofseriesIndonesian Journal of Electrical Engineering and Computer Science;Vol. 21, No. 2, February 2021
dc.subjectAda boost ensembleen_US
dc.subjectBagging ensembleen_US
dc.subjectGenetic algorithmen_US
dc.subjectMalaria vectoren_US
dc.subjectRNA-Seqen_US
dc.subjectNigeriaen_US
dc.subjectACE: Applied Informatics and Communicationen_US
dc.subjectCovenant Universityen_US
dc.subjectDigital Developmenten_US
dc.subjectOlatunji J. Okesolaen_US
dc.titlePredicting RNA-Seq data using genetic algorithm and ensemble classification algorithmsen_US
dc.typeArticleen_US
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