An ICA-ensemble learning approaches for prediction of RNAseq malaria vector gene expression data classification
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Date
2020-09-23
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Electrical and Computer Engineering (IJECE)
Abstract
Malaria 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.
Description
Keywords
Ensemble classifier, ICA, Malaria vector, RNA-seq, Nigeria, ACE: Applied Informatics and Communication, Covenant University, Digital Development, Charity Aremu4