Improved Protein-ligand Prediction Using Kernel Weighted Canonical Correlation Analysis

• Item type: Trans(1)
• 公開日: 2013-06-24
• タイトル: Improved Protein-ligand Prediction Using Kernel Weighted Canonical Correlation Analysis
• タイトル / 言語: en
• タイトル: Improved Protein-ligand Prediction Using Kernel Weighted Canonical Correlation Analysis
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Original Paper] canonical correlation analysis, kernel methods, protein-ligand interaction, support vector machines
• 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: Department of Computer Science, Graduate School of Engineering, Gunma University
• 著者所属: Department of Computer Science, Graduate School of Engineering, Gunma University
• 著者所属: Institute of Mathematics, College of Science, University of the Philippines
• 著者所属(英): en
• 著者所属(英): Department of Computer Science, Graduate School of Engineering, Gunma University
• 著者所属(英): en
• 著者所属(英): Department of Computer Science, Graduate School of Engineering, Gunma University
• 著者所属(英): en
• 著者所属(英): Institute of Mathematics, College of Science, University of the Philippines
• 著者名: Raissa, Relator; Tsuyoshi, Kato; Richard, Lemence
• 著者名(英): Raissa, Relator; Tsuyoshi, Kato; Richard, Lemence
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Protein-ligand interaction prediction plays an important role in drug design and discovery. However, wet lab procedures are inherently time consuming and expensive due to the vast number of candidate compounds and target genes. Hence, computational approaches became imperative and have become popular due to their promising results and practicality. Such methods require high accuracy and precision outputs for them to be useful, thus, the problem of devising such an algorithm remains very challenging. In this paper we propose an algorithm employing both support vector machines (SVM) and an extension of canonical correlation analysis (CCA). Following assumptions of recent chemogenomic approaches, we explore the effects of incorporating bias on similarity of compounds. We introduce kernel weighted CCA as a means of uncovering any underlying relationship between similarity of ligands and known ligands of target proteins. Experimental results indicate statistically significant improvement in the area under the ROC curve (AUC) and F-measure values obtained as opposed to those gathered when only SVM, or SVM with kernel CCA is employed, which translates to better quality of prediction.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Protein-ligand interaction prediction plays an important role in drug design and discovery. However, wet lab procedures are inherently time consuming and expensive due to the vast number of candidate compounds and target genes. Hence, computational approaches became imperative and have become popular due to their promising results and practicality. Such methods require high accuracy and precision outputs for them to be useful, thus, the problem of devising such an algorithm remains very challenging. In this paper we propose an algorithm employing both support vector machines (SVM) and an extension of canonical correlation analysis (CCA). Following assumptions of recent chemogenomic approaches, we explore the effects of incorporating bias on similarity of compounds. We introduce kernel weighted CCA as a means of uncovering any underlying relationship between similarity of ligands and known ligands of target proteins. Experimental results indicate statistically significant improvement in the area under the ROC curve (AUC) and F-measure values obtained as opposed to those gathered when only SVM, or SVM with kernel CCA is employed, which translates to better quality of prediction.
• 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 6, p. 18-28, 発行日 2013-06-24
• 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