TY - JOUR T1 - Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data JF - bioRxiv DO - 10.1101/120592 SP - 120592 AU - Almas Jabeen AU - Nadeem Ahmad AU - Khalid Raza Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/03/26/120592.abstract N2 - 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. ER -