ダウンロード数: 47
このアイテムのファイル:
ファイル | 記述 | サイズ | フォーマット | |
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978-981-16-4457-3_20.pdf | 1.29 MB | Adobe PDF | 見る/開く |
タイトル: | Machine Learning for Metabolic Identification |
著者: | Nguyen, Dai Hai Nguyen, Canh Hao Mamitsuka, Hiroshi https://orcid.org/0000-0002-6607-5617 (unconfirmed) |
著者名の別形: | 馬見塚, 拓 |
キーワード: | Machine learning Metabolic identification Mass spectrometry (MS) Electron ionization (EI) Electrospray ionization (ESI) |
発行日: | 2021 |
出版者: | Springer, Singapore |
誌名: | Creative Complex Systems |
開始ページ: | 329 |
終了ページ: | 350 |
抄録: | 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. |
記述: | Part of the book series: Creative Economy (CRE) |
著作権等: | This is an author's accepted manuscript (AAM) of a chapter published in 'Creative Complex Systems'. The final authenticated version is available online at: https://doi.org/10.1007/978-981-16-4457-3_20. The full-text file will be made open to the public on 27 October 2023 in accordance with publisher's 'Terms and Conditions for Self-Archiving' This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
URI: | http://hdl.handle.net/2433/276134 |
DOI(出版社版): | 10.1007/978-981-16-4457-3_20 |
出現コレクション: | 学術雑誌掲載論文等 |
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