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Machine Learning for Health Informatics pp 319–336Cite as

Network-Guided Biomarker Discovery

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9605)

Abstract

Identifying measurable genetic indicators (or biomarkers) of a specific condition of a biological system is a key element of precision medicine. Indeed it allows to tailor diagnostic, prognostic and treatment choice to individual characteristics of a patient. In machine learning terms, biomarker discovery can be framed as a feature selection problem on whole-genome data sets. However, classical feature selection methods are usually underpowered to process these data sets, which contain orders of magnitude more features than samples. This can be addressed by making the assumption that genetic features that are linked on a biological network are more likely to work jointly towards explaining the phenotype of interest. We review here three families of methods for feature selection that integrate prior knowledge in the form of networks.

Keywords

  • Biological networks
  • Structured sparsity
  • Feature selection
  • Biomarker discovery

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Fig. 1.

Notes

  1. 1.

    https://github.com/chagaz/scones.

  2. 2.

    https://github.com/chagaz/sfan.

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Authors and Affiliations

  1. CBIO-Centre for Computational Biology, MINES ParisTech, PSL-Research University, 35 rue St Honoré, 77300, Fontainebleau, France

    Chloé-Agathe Azencott

  2. Institut Curie, 75248, Paris Cedex 05, France

    Chloé-Agathe Azencott

  3. INSERM, U900, 75248, Paris Cedex 05, France

    Chloé-Agathe Azencott

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  1. Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

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Azencott, CA. (2016). Network-Guided Biomarker Discovery. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science(), vol 9605. Springer, Cham. https://doi.org/10.1007/978-3-319-50478-0_16

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