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Machine Learning for Metabolic Identification

In: Creative Complex Systems

Author

Listed:
  • Dai Hai Nguyen

    (The University of Tokyo
    Kyoto University)

  • Canh Hao Nguyen

    (Kyoto University)

  • Hiroshi Mamitsuka

    (Kyoto University
    Aalto University)

Abstract

Metabolic identification is an essential part of metabolomics to understand biochemical characteristics of metabolites, which are small molecules that play important functions in biological systems. However, this field remains challenging with many unknown metabolites in existence. Mass spectrometry (MS)Mass spectrometry (MS) is a common technology that deals with such small molecules. Over recent decades, many methods have been proposed for MSMass spectrometry (MS)-based metabolite identification, but machine learningMachine learning has been a key process in recent progress in metabolite identification. This chapter provides a survey on computational methods for metabolic identification with the focus on machine learningMachine learning, with a discussion on potential improvements for this task.

Suggested Citation

  • Dai Hai Nguyen & Canh Hao Nguyen & Hiroshi Mamitsuka, 2021. "Machine Learning for Metabolic Identification," Creative Economy, in: Kazuo Nishimura & Masatoshi Murase & Kazuyoshi Yoshimura (ed.), Creative Complex Systems, chapter 0, pages 329-350, Springer.
    • Handle: RePEc:spr:crechp:978-981-16-4457-3_20
    DOI: 10.1007/978-981-16-4457-3_20
    as

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