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Better Interpretable Models for Proteomics Data Analysis Using Rule-Based Mining

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Towards Integrative Machine Learning and Knowledge Extraction

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10344))

Abstract

Recent advances in -omics technology has yielded in large data-sets in many areas of biology, such as mass spectrometry based proteomics. However, analyzing this data is still a challenging task mainly due to the very high dimensionality and high noise content of the data. One of the main objectives of the analysis is the identification of relevant patterns (or features) which can be used for classification of new samples to healthy or diseased. So, a method is required to find easily interpretable models from this data.

To gain the above mentioned goal, we have adapted the disjunctive association rule mining algorithm, TitanicOR, to identify emerging patterns from our mass spectrometry proteomics data-sets. Comparison to five state-of-the-art methods shows that our method is advantageous them in terms of identifying the inter-dependency between the features and the TP-rate and precision of the features selected. We further demonstrate the applicability of our algorithm to one previously published clinical data-set.

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Authors and Affiliations

  1. Zuse Institute Berlin, Takustr. 7, 14195, Berlin, Germany

    Fahrnaz Jayrannejad & Tim O. F. Conrad

  2. Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany

    Tim O. F. Conrad

Authors
  1. Fahrnaz Jayrannejad
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  2. Tim O. F. Conrad
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Correspondence to Fahrnaz Jayrannejad .

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Editors and Affiliations

  1. Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. University of Alberta, Edmonton, Alberta, Canada

    Randy Goebel

  3. Bologna University, Bologna, Italy

    Massimo Ferri

  4. Coventry University, Coventry, United Kingdom

    Vasile Palade

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Jayrannejad, F., Conrad, T.O.F. (2017). Better Interpretable Models for Proteomics Data Analysis Using Rule-Based Mining. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds) Towards Integrative Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science(), vol 10344. Springer, Cham. https://doi.org/10.1007/978-3-319-69775-8_4

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  • DOI: https://doi.org/10.1007/978-3-319-69775-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69774-1

  • Online ISBN: 978-3-319-69775-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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