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Adopting Multivariate Nonparametric Tools to Determine Genotype-Phenotype Interactions in Health and Disease

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Metabonomics and Gut Microbiota in Nutrition and Disease

Part of the book series: Molecular and Integrative Toxicology ((MOLECUL))

Abstract

This chapter describes the role of machine learning approaches such as random forests in holistic discovery applications and provides a background for its better understanding. Their suitability for feature selection, data integration, and network modelling are also evaluated through recent examples in the literature. These examples cover a variety of fields, ranging from ecology to metabolomics.

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Authors and Affiliations

  1. Analytical Sciences, Applied Mathematics, Nestec Ltd., Nestlé Research Centre, 44, Route du Jorat 57, CH-1000, Lausanne 26, Switzerland

    Ivan Montoliu

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  1. Ivan Montoliu
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Correspondence to Ivan Montoliu .

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Editors and Affiliations

  1. Analytical Sciences Competence Pillar, Nestec SA, Nestlé Research Center, Lausanne, Switzerland

    Sunil Kochhar

  2. Molecular Biomarkers, Nestlé Institute of Health Sciences, Lausanne, Switzerland

    François-Pierre Martin

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Montoliu, I. (2015). Adopting Multivariate Nonparametric Tools to Determine Genotype-Phenotype Interactions in Health and Disease. In: Kochhar, S., Martin, FP. (eds) Metabonomics and Gut Microbiota in Nutrition and Disease. Molecular and Integrative Toxicology. Springer, London. https://doi.org/10.1007/978-1-4471-6539-2_3

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