An Efficient PCA Ensemble Learning Approach for Prediction of RNA-Seq Malaria Vector Gene Expression Data Classification
| dc.contributor.author | Arowolo, Micheal Olaolu | |
| dc.contributor.author | Adebiyi, Marion O. | |
| dc.contributor.author | Adebiy, Ayodele A. | |
| dc.date.accessioned | 2023-08-31T16:10:12Z | |
| dc.date.available | 2023-08-31T16:10:12Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Malaria parasites adopt outstanding variation of life phases as they evolve through manifold mosquito vector atmospheres. Transcriptomes of thousands of individual parasites exists. Ribonucleic acid sequencing (RNA-seq) is a widespread method for gene expression which has resulted into improved understandings of genetical queries. RNA-seq compute transcripts of gene expressions. RNA-seq data necessitates analytical improvements of machine learning techniques. Several learning approached have been proposed by researchers for analysing biological data. In this study, PCA feature extraction algorithm is used to fetch latent components out of a high dimensional malaria vector RNA-seq dataset, and evaluates it classification performance using an Ensemble classification algorithm. The effectiveness of this experiment is validated on aa mosquito anopheles gambiae RNA-Seq dataset. The experiment result achieved a relevant performance metrics with a classification accuracy of 93.3%. | en_US |
| dc.description.sponsorship | ACE: Applied Informatics and Communication | en_US |
| dc.identifier.issn | 0974-3154 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/2112 | |
| dc.language.iso | en | en_US |
| dc.publisher | International Journal of Engineering Research and Technology | en_US |
| dc.relation.ispartofseries | International Journal of Engineering Research and Technology;Volume 13, Number 1 (2020) | |
| dc.subject | RNA-Seq | en_US |
| dc.subject | PCA | en_US |
| dc.subject | Ensemble Cassification | en_US |
| dc.subject | Malaria Vector | en_US |
| dc.subject | Nigeria | en_US |
| dc.subject | ACE: Applied Informatics and Communication | en_US |
| dc.subject | Covenant University | en_US |
| dc.subject | Digital Development | en_US |
| dc.title | An Efficient PCA Ensemble Learning Approach for Prediction of RNA-Seq Malaria Vector Gene Expression Data Classification | en_US |
| dc.type | Article | en_US |