Predicting RNA-Seq data using genetic algorithm and ensemble classification algorithms
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Date
2020-05-03
Journal Title
Journal ISSN
Volume Title
Publisher
Indonesian Journal of Electrical Engineering and Computer Science
Abstract
Malaria 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%
Description
Keywords
Ada boost ensemble, Bagging ensemble, Genetic algorithm, Malaria vector, RNA-Seq, Nigeria, ACE: Applied Informatics and Communication, Covenant University, Digital Development, Olatunji J. Okesola