A survey of dimension reduction and classification methods for RNA-Seq data on malaria vector

Arowolo, Michael Olaolu and Adebiyi, Marion and Aremu, C. O. and Adebiyi, A. A. (2021) A survey of dimension reduction and classification methods for RNA-Seq data on malaria vector. Journal of Big Data, 8 (1). ISSN 2196-1115

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Abstract

Recently unique spans of genetic data are produced by researchers, there is a trend in genetic exploration using machine learning integrated analysis and virtual combina�tion of adaptive data into the solution of classifcation problems. Detection of ailments and infections at early stage is of key concern and a huge challenge for researchers in the feld of machine learning classifcation and bioinformatics. Considerate genes contributing to diseases are of huge dispute to a lot of researchers. This study reviews various works on Dimensionality reduction techniques for reducing sets of features that groups data efectively with less computational processing time and classifcation methods that contributes to the advances of RNA-Sequencing approach.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User:	Mr Uchechukwu F. Ekpendu
Date Deposited:	02 Jul 2021 10:50
Last Modified:	02 Jul 2021 10:50
URI:	https://eprints.lmu.edu.ng/id/eprint/3156

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