Projects per year
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 language | English |
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Title of host publication | Creative Complex Systems |
Publisher | Springer |
Pages | 329-350 |
Number of pages | 22 |
ISBN (Electronic) | 978-981-16-4457-3 |
ISBN (Print) | 978-981-16-4456-6 |
DOIs | |
Publication status | Published - 2021 |
MoE publication type | A3 Book section, Chapters in research books |
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Dive into the research topics of 'Machine Learning for Metabolic Identification'. Together they form a unique fingerprint.Projects
- 1 Finished
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FiDiPro - Machine Learning for Augmented Science and Knowledge Work
Kaski, S. (Principal investigator)
01/01/2015 → 31/12/2018
Project: Business Finland: Other research funding