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Using GP Is NEAT: Evolving Compositional Pattern Production Functions Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Analyzing Feature Importance for Metabolomics Using Genetic Programming

Authors : Ting Hu, Karoliina Oksanen, Weidong Zhang, Edward Randell, Andrew Furey, Guangju Zhai

Published in: Genetic Programming

Publisher: Springer International Publishing

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Abstract

The emerging and fast-developing field of metabolomics examines the abundance of small-molecule metabolites in body fluids to study the cellular processes related to how the human body responds to genetic and environmental perturbations. Considering the complexity of metabolism, metabolites and their represented cellular processes can correlate and synergistically contribute to a phenotypic status. Genetic programming (GP) provides advanced analytical instruments for the investigation of multifactorial causes of metabolic diseases. In this article, we analyzed a population-based metabolomics dataset on osteoarthritis (OA) and developed a Linear GP (LGP) algorithm to search classification models that can best predict the disease outcome, as well as to identify the most important metabolic markers associated with the disease. The LGP algorithm was able to evolve prediction models with high accuracies especially with a more focused search using a reduced feature set that only includes potentially relevant metabolites. We also identified a set of key metabolic markers that may improve our understanding of the biochemistry and pathogenesis of the disease.

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previous chapter Pruning Techniques for Mixed Ensembles of Genetic Programming Models
next chapter Generating Redundant Features with Unsupervised Multi-tree Genetic Programming
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Metadata
Title
Analyzing Feature Importance for Metabolomics Using Genetic Programming
Authors
Ting Hu
Karoliina Oksanen
Weidong Zhang
Edward Randell
Andrew Furey
Guangju Zhai
Copyright Year
2018
Book
Genetic Programming

Print ISBN: 978-3-319-77552-4

Electronic ISBN: 978-3-319-77553-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77553-1_5

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