Review of machine learning algorithms in differential expression analysis

Irina Kuznetsova, Yuliya V. Karpievitch, Aleksandra Filipovska, Artur Lugmayr, Andreas Holzinger

Research output: Chapter in Book/Conference paperConference paperpeer-review


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 (RNA-Seq) 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.

Original languageEnglish
Title of host publicationProceedings of the 9th Workshop on Semantic Ambient Media Experiences
Subtitle of host publicationVisualisation, Emerging Media, and User Experience
EditorsArtur Lugmayr, Richard Seale, Andrew Woods, Eunice Sari, Adi Tedjasaputra
Place of PublicationAustralia
PublisherInternational Ambient Media Association (iAMEA)
Number of pages14
ISBN (Electronic)9781510800168
Publication statusPublished - 2017
Event9th Workshop on Semantic Ambient Media Experiences - Perth, Australia
Duration: 10 Nov 201611 Nov 2016

Publication series

Name International Series on Information Systems and Management in Creative eMedia
ISSN (Print)2341-5584
ISSN (Electronic)2341-5576


Conference9th Workshop on Semantic Ambient Media Experiences


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