RT Journal Article
SR Electronic
T1 Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 120592
DO 10.1101/120592
A1 Almas Jabeen
A1 Nadeem Ahmad
A1 Khalid Raza
YR 2017
UL http://biorxiv.org/content/early/2017/03/26/120592.abstract
AB RNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. Various computational tools have been developed for studying pathogen or virus from RNA-Seq data by classifying them according to the attributes in several predefined classes, but still computational tools and approaches to analyze complex datasets are still lacking. The development of classification models is highly recommended for disease diagnosis and classification, disease monitoring at molecular level as well as researching for potential disease biomarkers. In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. Advancements in bioinformatics, along with developments in machine learning based classification, would provide powerful toolboxes for classifying transcriptome information available through RNA-Seq data.