Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities
Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/464093 |
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| _version_ | 1849472843787534336 |
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| author | Bin Liu Bingquan Liu Fule Liu Xiaolong Wang |
| author_facet | Bin Liu Bingquan Liu Fule Liu Xiaolong Wang |
| author_sort | Bin Liu |
| collection | DOAJ |
| description | Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC. |
| format | Article |
| id | doaj-art-da4e73d772d347a8aab38f5d1577f02e |
| 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-da4e73d772d347a8aab38f5d1577f02e2025-08-20T03:24:25ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/464093464093Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based PropensitiesBin Liu0Bingquan Liu1Fule Liu2Xiaolong Wang3School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, ChinaIdentification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC.http://dx.doi.org/10.1155/2014/464093 |
| spellingShingle | Bin Liu Bingquan Liu Fule Liu Xiaolong Wang Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities The Scientific World Journal |
| title | Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities |
| title_full | Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities |
| title_fullStr | Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities |
| title_full_unstemmed | Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities |
| title_short | Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities |
| title_sort | protein binding site prediction by combining hidden markov support vector machine and profile based propensities |
| url | http://dx.doi.org/10.1155/2014/464093 |
| work_keys_str_mv | AT binliu proteinbindingsitepredictionbycombininghiddenmarkovsupportvectormachineandprofilebasedpropensities AT bingquanliu proteinbindingsitepredictionbycombininghiddenmarkovsupportvectormachineandprofilebasedpropensities AT fuleliu proteinbindingsitepredictionbycombininghiddenmarkovsupportvectormachineandprofilebasedpropensities AT xiaolongwang proteinbindingsitepredictionbycombininghiddenmarkovsupportvectormachineandprofilebasedpropensities |