In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNASeq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments.
@article{arxiv.1707.09837,
title = {Review of Machine Learning Algorithms in Differential Expression Analysis},
author = {Irina Kuznetsova and Yuliya V Karpievitch and Aleksandra Filipovska and Artur Lugmayr and Andreas Holzinger},
journal= {arXiv preprint arXiv:1707.09837},
year = {2017}
}