  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.
