Open AccessAn ICA-ensemble learning approaches for prediction of RNA-seq malaria vector gene expression data classification
Author(s) -
Micheal Olaolu Arowolo,
Marion O. Adebiyi,
Ayodele Ariyo Adebiyi,
Charity Aremu
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i2.pp1561-1569
Subject(s) - rna seq , anopheles gambiae , artificial intelligence , computer science , support vector machine , rna , ensemble learning , transcriptome , feature (linguistics) , gene , feature vector , machine learning , computational biology , pattern recognition (psychology) , gene expression , data mining , biology , malaria , genetics , linguistics , philosophy , immunology
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. (RNA-seq) Ribonucleic acid sequencing 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.