Machine Learning for Metabolic Identification

Dai Hai Nguyen (), Canh Hao Nguyen () and Hiroshi Mamitsuka ()
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Dai Hai Nguyen: The University of Tokyo
Canh Hao Nguyen: Kyoto University
Hiroshi Mamitsuka: Kyoto University

Chapter Chapter 20 in Creative Complex Systems, 2021, pp 329-350 from Springer

Abstract: 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.

Keywords: Machine learning; Metabolic identification; Mass spectrometry (MS); Electron ionization (EI); Electrospray ionization (ESI) (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:crechp:978-981-16-4457-3_20

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DOI: 10.1007/978-981-16-4457-3_20

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