An Efficient PCA Ensemble Learning Approach for Prediction of RNA-Seq Malaria Vector Gene Expression Data Classification
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Engineering Research and Technology
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%.
Description
Keywords
RNA-Seq, PCA, Ensemble Cassification, Malaria Vector, Nigeria, ACE: Applied Informatics and Communication, Covenant University, Digital Development