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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 |
完全メタデータレコード
DCフィールド | 値 | 言語 |
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dc.contributor.author | Nguyen, Dai Hai | en |
dc.contributor.author | Nguyen, Canh Hao | en |
dc.contributor.author | Mamitsuka, Hiroshi | en |
dc.contributor.alternative | 馬見塚, 拓 | ja |
dc.date.accessioned | 2022-09-06T05:02:31Z | - |
dc.date.available | 2022-09-06T05:02:31Z | - |
dc.date.issued | 2021 | - |
dc.identifier.isbn | 9789811644573 | - |
dc.identifier.uri | http://hdl.handle.net/2433/276134 | - |
dc.description | Part of the book series: Creative Economy (CRE) | en |
dc.description.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. | en |
dc.language.iso | eng | - |
dc.publisher | Springer, Singapore | en |
dc.rights | 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. | en |
dc.rights | 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' | en |
dc.rights | 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。 | en |
dc.subject | Machine learning | en |
dc.subject | Metabolic identification | en |
dc.subject | Mass spectrometry (MS) | en |
dc.subject | Electron ionization (EI) | en |
dc.subject | Electrospray ionization (ESI) | en |
dc.title | Machine Learning for Metabolic Identification | en |
dc.type | book part | - |
dc.type.niitype | Book | - |
dc.identifier.jtitle | Creative Complex Systems | en |
dc.identifier.spage | 329 | - |
dc.identifier.epage | 350 | - |
dc.relation.doi | 10.1007/978-981-16-4457-3_20 | - |
dc.textversion | author | - |
dcterms.accessRights | open access | - |
datacite.date.available | 2023-10-27 | - |
datacite.awardNumber | 19J14714 | - |
datacite.awardNumber | 18K11434 | - |
datacite.awardNumber | JPMJAC1503 | - |
datacite.awardNumber | 16H02868 | - |
datacite.awardNumber | 19H04169 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-19J14714/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-18K11434/ | - |
datacite.awardNumber.uri | https://projectdb.jst.go.jp/grant/JST-PROJECT-15666456/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-16H02868/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-19H04169/ | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 科学技術振興機構 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | 質量分析のための機械学習手法構築 | ja |
jpcoar.awardTitle | Machine Learning on Large Graphs | en |
jpcoar.awardTitle | 濃厚ポリマーブラシのレジリエンシー強化とトライボロジー応用 | ja |
jpcoar.awardTitle | 複数行列データからのデータ因子構造推定 | ja |
jpcoar.awardTitle | 複数のテンソルからの効率的なデータ構造推定 | ja |
出現コレクション: | 学術雑誌掲載論文等 |
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