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Protein-compound interaction prediction using microbial chemical communication network
https://ipsj.ixsq.nii.ac.jp/records/229529
https://ipsj.ixsq.nii.ac.jp/records/229529fdfead0a-2945-4afb-9eb3-cbd2f89a8687
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-BIO23076007.pdf (1.1 MB)
2025年11月22日からダウンロード可能です。
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Copyright (c) 2023 by the Information Processing Society of Japan
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| 非会員：¥660, IPSJ:学会員：¥330, BIO:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
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| 公開日 | 2023-11-22 | |||||||||
| タイトル | ||||||||||
| タイトル | Protein-compound interaction prediction using microbial chemical communication network | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Protein-compound interaction prediction using microbial chemical communication network | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||
| 資源タイプ | technical report | |||||||||
| 著者所属 | ||||||||||
| The University of Tokyo | ||||||||||
| 著者所属 | ||||||||||
| The University of Tokyo／National Institute of Advanced Industrial Science and Technology (AIST)／AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL)／Kitasato University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| The University of Tokyo | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| The University of Tokyo / National Institute of Advanced Industrial Science and Technology (AIST) / AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) / Kitasato University | ||||||||||
| 著者名 |
Hongyi, Shen
× Hongyi, Shen
× Yutaka, Saito
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| 著者名(英) |
Hongyi, Shen
× Hongyi, Shen
× Yutaka, Saito
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Protein-compound interaction prediction is an important problem in drug discovery. Numerous machine learning methods have been proposed using protein sequences and compound structures as features. Several methods have used biological network information as additional features including protein-protein interactions and compound bioactivities. However, previous studies have only used network data from mammals such as human and mouse. Here we develop a new method for protein-compound interaction prediction that uses features learned from the relationships between microorganisms and secondary metabolites in nature (microbial chemical communication network; MCCN). We used node2vec representation learning to extract compound features from the MCCN, and deep canonical correlation analysis (CCA) to obtain the features for compounds not included in the MCCN. By incorporating these MCCN-derived features into an existing protein-compound interaction prediction method, we showed that prediction performance was improved in several benchmark experiments. We also discussed how to improve our method by incorporating microbiome co-occurrence information into the MCCN. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Protein-compound interaction prediction is an important problem in drug discovery. Numerous machine learning methods have been proposed using protein sequences and compound structures as features. Several methods have used biological network information as additional features including protein-protein interactions and compound bioactivities. However, previous studies have only used network data from mammals such as human and mouse. Here we develop a new method for protein-compound interaction prediction that uses features learned from the relationships between microorganisms and secondary metabolites in nature (microbial chemical communication network; MCCN). We used node2vec representation learning to extract compound features from the MCCN, and deep canonical correlation analysis (CCA) to obtain the features for compounds not included in the MCCN. By incorporating these MCCN-derived features into an existing protein-compound interaction prediction method, we showed that prediction performance was improved in several benchmark experiments. We also discussed how to improve our method by incorporating microbiome co-occurrence information into the MCCN. | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA12055912 | |||||||||
| 書誌情報 |
研究報告バイオ情報学（BIO） 巻 2023-BIO-76, 号 7, p. 1-5, 発行日 2023-11-22 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2188-8590 | |||||||||
| Notice | ||||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
