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    Review of machine learning algorithms in differential expression analysis

    Access Status
    Fulltext not available
    Authors
    Kuznetsova, I.
    Karpievitch, Y.
    Filipovska, A.
    Lugmayr, Artur
    Holzinger, A.
    Date
    2016
    Type
    Conference Paper
    
    Metadata
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    Citation
    Kuznetsova, I. and Karpievitch, Y. and Filipovska, A. and Lugmayr, A. and Holzinger, A. 2016. Review of machine learning algorithms in differential expression analysis, pp. 11-24.
    Source Title
    International series on information systems and management in creative eMedia
    ISBN
    9781510800168
    School
    Department of Film and Television
    URI
    http://hdl.handle.net/20.500.11937/56285
    Collection
    • Curtin Research Publications
    Abstract

    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.

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