Machine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq Data

Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 26)

Abstract

Ribonucleic acid sequencing (RNA-Seq) measures the expression levels of several transcripts simultaneously. The readings can be gene, exon, or other regions of interest. Various computational tools have been developed for studying pathogens or viruses from RNA-Seq data by classifying them according to the attributes in several pre-defined classes. However, computational tools and approaches to analyzing complex datasets are still lacking. The development of classification models is highly recommended for the diagnosis and classification of diseases, disease monitoring at the molecular level and research into potential disease biomarkers. In this chapter, we discuss various machine learning approaches for RNA-Seq data classification and their implementation. These advancements in bioinformatics, along with developments in machine learning-based classification, would provide powerful toolboxes for the classification of transcriptome information available through RNA-Seq data.

Keywords

RNA-Seq data Deep learning Deep neural networks Supervised Unsupervised Classification Clustering Support vector machine (SVM) BagSVM Classification and regression trees (CART) Random forest Feature selection 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of BiosciencesJamia Millia IslamiaNew DelhiIndia
  2. 2.Department of Computer ScienceJamia Millia IslamiaNew DelhiIndia

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