Machine Learning for Metabolic Identification

Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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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) is a common technology that deals with such small molecules. Over recent decades, many methods have been proposed for MS-based metabolite identification, but machine 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 learning, with a discussion on potential improvements for this task.
Original languageEnglish
Title of host publicationCreative Complex Systems
PublisherSpringer
Pages329-350
Number of pages22
ISBN (Electronic)978-981-16-4457-3
ISBN (Print)978-981-16-4456-6
DOIs
Publication statusPublished - 2021
MoE publication typeA3 Book section, Chapters in research books

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