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タイトル: | Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression |
著者: | Kamada, Mayumi Sakuma, Yusuke Hayashida, Morihiro Akutsu, Tatsuya https://orcid.org/0000-0001-9763-797X (unconfirmed) |
著者名の別形: | 林田, 守広 |
発行日: | 24-Jun-2014 |
出版者: | Hindawi Publishing Corporation |
誌名: | The Scientific World Journal |
巻: | 2014 |
論文番号: | 240673 |
抄録: | Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method. |
著作権等: | Copyright © 2014 Mayumi Kamada et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
URI: | http://hdl.handle.net/2433/189275 |
DOI(出版社版): | 10.1155/2014/240673 |
PubMed ID: | 25093200 |
出現コレクション: | 学術雑誌掲載論文等 |
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