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Using ILP to Identify Pathway Activation Patterns in Systems Biology

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Inductive Logic Programming (ILP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9575))

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Abstract

We show a logical aggregation method that, combined with propositionalization methods, can construct novel structured biological features from gene expression data. We do this to gain understanding of pathway mechanisms, for instance, those associated with a particular disease. We illustrate this method on the task of distinguishing between two types of lung cancer; Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AC). We identify pathway activation patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist.

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Acknowledgments

LACM received funding from the Medical Research Council (MC_UU_12013/8).

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Authors and Affiliations

  1. Department of Informatics, King’s College London, Strand, London, UK

    Samuel R. Neaves & Sophia Tsoka

  2. MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, University of Bristol, Bristol, UK

    Louise A. C. Millard

  3. Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, UK

    Louise A. C. Millard

Authors
  1. Samuel R. Neaves
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  2. Louise A. C. Millard
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  3. Sophia Tsoka
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Corresponding author

Correspondence to Samuel R. Neaves .

Editor information

Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Katsumi Inoue

  2. Tokyo University of Science, Noda, Japan

    Hayato Ohwada

  3. Kyoto University, Kyoto, Japan

    Akihiro Yamamoto

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Neaves, S.R., Millard, L.A.C., Tsoka, S. (2016). Using ILP to Identify Pathway Activation Patterns in Systems Biology. In: Inoue, K., Ohwada, H., Yamamoto, A. (eds) Inductive Logic Programming. ILP 2015. Lecture Notes in Computer Science(), vol 9575. Springer, Cham. https://doi.org/10.1007/978-3-319-40566-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-40566-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40565-0

  • Online ISBN: 978-3-319-40566-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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