Machine learning applications in proteomics research: How the past can boost the future

Authors

  • Pieter Kelchtermans,

    1. Department of Medical Protein Research, VIB, Ghent, Belgium
    2. Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
    3. Flemish Institute for Technological Research (VITO), Mol, Belgium
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  • Wout Bittremieux,

    1. Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
    2. Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp, Antwerp, Belgium
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  • Kurt De Grave,

    1. Department of Computer Science, KU Leuven, Heverlee, Belgium
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  • Sven Degroeve,

    1. Department of Medical Protein Research, VIB, Ghent, Belgium
    2. Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
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  • Jan Ramon,

    1. Department of Computer Science, KU Leuven, Heverlee, Belgium
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  • Kris Laukens,

    1. Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
    2. Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp, Antwerp, Belgium
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  • Dirk Valkenborg,

    1. Flemish Institute for Technological Research (VITO), Mol, Belgium
    2. I-BioStat, Hasselt University, Belgium
    3. CFP-CeProMa, University of Antwerp, Belgium
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  • Harald Barsnes,

    1. Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
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  • Lennart Martens

    Corresponding author
    1. Department of Medical Protein Research, VIB, Ghent, Belgium
    2. Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
    • Correspondence: Professor Lennart Martens, Department of Medical Protein Research and Biochemistry, VIB and Faculty of Medicine and Health Sciences, Ghent University, A. Baertsoenkaai 3, B-9000 Ghent, Belgium

      E-mail: lennart.martens@ugent.be

      Fax: +32-92649484

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Abstract

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.

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