Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression
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 involve...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/240673 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832545613765410816 |
|---|---|
| author | Mayumi Kamada Yusuke Sakuma Morihiro Hayashida Tatsuya Akutsu |
| author_facet | Mayumi Kamada Yusuke Sakuma Morihiro Hayashida Tatsuya Akutsu |
| author_sort | Mayumi Kamada |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f358e0baf29d46819b8d716aa0a0c723 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-f358e0baf29d46819b8d716aa0a0c7232025-02-03T07:25:20ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/240673240673Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised RegressionMayumi Kamada0Yusuke Sakuma1Morihiro Hayashida2Tatsuya Akutsu3Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JapanJapan Ichiba Section Development Unit, Rakuten Inc., 4-12-3 Higashi-shinagawa, Shinagawa-ku, Tokyo 140-0002, JapanBioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, JapanBioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, JapanProteins 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.http://dx.doi.org/10.1155/2014/240673 |
| spellingShingle | Mayumi Kamada Yusuke Sakuma Morihiro Hayashida Tatsuya Akutsu Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression The Scientific World Journal |
| title | Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression |
| title_full | Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression |
| title_fullStr | Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression |
| title_full_unstemmed | Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression |
| title_short | Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression |
| title_sort | prediction of protein protein interaction strength using domain features with supervised regression |
| url | http://dx.doi.org/10.1155/2014/240673 |
| work_keys_str_mv | AT mayumikamada predictionofproteinproteininteractionstrengthusingdomainfeatureswithsupervisedregression AT yusukesakuma predictionofproteinproteininteractionstrengthusingdomainfeatureswithsupervisedregression AT morihirohayashida predictionofproteinproteininteractionstrengthusingdomainfeatureswithsupervisedregression AT tatsuyaakutsu predictionofproteinproteininteractionstrengthusingdomainfeatureswithsupervisedregression |