Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms

Micheal Olaolu Arowolo, Marion O. Adebiyi, Ayodele A. Adebiyi, Olatunji J. Okesola


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%.


Ada boost ensemble; Bagging ensemble; Genetic algorithm; Malaria vector; RNA-Seq

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