A survey of dimension reduction and classification methods for RNA-Seq data on malaria vector
Micheal Olaolu Arowolo, Marion O. Adebiyi, Charity Aremu, Ayodele A. Adebiyi
Abstract
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 combination of adaptive data into the solution of classification problems. Detection of ailments and infections at early stage is of key concern and a huge challenge for researchers in the field of machine learning classification 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 effectively with less computational processing time and classification methods that contributes to the advances of RNA-Sequencing approach.