An ICA-ensemble learning approaches for prediction of RNAseq malaria vector gene expression data classification

Simple item page
dc.contributor.authorArowolo, Micheal Olaolu
dc.contributor.authorAdebiyi, Marion O.
dc.contributor.authorAdebiyi, Ayodele A.
dc.date.accessioned2023-08-31T14:39:52Z
dc.date.available2023-08-31T14:39:52Z
dc.date.issued2020-09-23
dc.description.abstractMalaria parasites introduce outstanding life-phase variations as they grow across multiple atmospheres of the mosquito vector. There are transcriptomes of several thousand different parasites. Ribonucleic acid sequencing (RNAseq) is a prevalent gene expression tool leading to better understanding of genetic interrogations. RNA-seq measures transcriptions of expressions of genes. Data from RNA-seq necessitate procedural enhancements in machine learning techniques. Researchers have suggested various approached learning for the study of biological data. This study works on ICA feature extraction algorithm to realize dormant components from a huge dimensional RNA-seq vector dataset, and estimates its classification performance, Ensemble classification algorithm is used in carrying out the experiment. This study is tested on RNA-seq mosquito anopheles gambiae dataset. The results of the experiment obtained an output metrics with a 93.3% classification accuracy.en_US
dc.description.sponsorshipACE: Applied Informatics and Communicationen_US
dc.identifier.issn2088-8708
dc.identifier.urihttp://hdl.handle.net/123456789/2108
dc.language.isoenen_US
dc.publisherInternational Journal of Electrical and Computer Engineering (IJECE)en_US
dc.relation.ispartofseriesInternational Journal of Electrical and Computer Engineering (IJECE);Vol. 11, No. 2, April 2021
dc.subjectEnsemble classifieren_US
dc.subjectICAen_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.subjectCharity Aremu4en_US
dc.titleAn ICA-ensemble learning approaches for prediction of RNAseq malaria vector gene expression data classificationen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
learning.pdf
Size:
674.45 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