Drug Clearance Pathway Prediction Based on Semi-supervised Learning

• Item type: Trans(1)
• 公開日: 2015-08-19
• タイトル: Drug Clearance Pathway Prediction Based on Semi-supervised Learning
• タイトル / 言語: en
• タイトル: Drug Clearance Pathway Prediction Based on Semi-supervised Learning
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Original Paper] drug clearance pathway prediction, drug discovery support, machine learning, semi-supervised learning
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Information Science and Engineering, Tokyo Institute of Technology
• 著者所属: Graduate School of Information Science and Engineering, Tokyo Institute of Technology
• 著者所属: Graduate School of Information Science and Engineering, Tokyo Institute of Technology
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Engineering, Tokyo Institute of Technology
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Engineering, Tokyo Institute of Technology
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Engineering, Tokyo Institute of Technology
• 著者名: Keisuke, Yanagisawa; Takashi, Ishida; Yutaka, Akiyama
• 著者名(英): Keisuke, Yanagisawa; Takashi, Ishida; Yutaka, Akiyama
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: It is necessary to confirm that a new drug can be appropriately cleared from the human body. However, checking the clearance pathway of a drug in the human body requires clinical trials, and therefore requires large cost. Thus, computational methods for drug clearance pathway prediction have been studied. The proposed prediction methods developed previously were based on a supervised learning algorithm, which requires clearance pathway information for all drugs in a training set as input labels. However, these data are often insufficient in its numbers because of the high cost of their acquisition. In this paper, we propose a new drug clearance pathway prediction method based on semi-supervised learning, which can use not only labeled data but also unlabeled data. We evaluated the effectiveness of our method, focusing on the cytochrome P450 2C19 enzyme, which is involved in one of the major clearance pathways.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: It is necessary to confirm that a new drug can be appropriately cleared from the human body. However, checking the clearance pathway of a drug in the human body requires clinical trials, and therefore requires large cost. Thus, computational methods for drug clearance pathway prediction have been studied. The proposed prediction methods developed previously were based on a supervised learning algorithm, which requires clearance pathway information for all drugs in a training set as input labels. However, these data are often insufficient in its numbers because of the high cost of their acquisition. In this paper, we propose a new drug clearance pathway prediction method based on semi-supervised learning, which can use not only labeled data but also unlabeled data. We evaluated the effectiveness of our method, focusing on the cytochrome P450 2C19 enzyme, which is involved in one of the major clearance pathways.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12177013
• 書誌情報: IPSJ Transactions on Bioinformatics （TBIO）; 巻 8, p. 21-27, 発行日 2015-08-19
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6679
• 出版者 / 言語: ja
• 出版者: 情報処理学会