Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM
Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, co...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-08-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/22/8/1366 |
| _version_ | 1829137406774738944 |
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| author | Yan-Bin Wang Zhu-Hong You Li-Ping Li Yu-An Huang Hai-Cheng Yi |
| author_facet | Yan-Bin Wang Zhu-Hong You Li-Ping Li Yu-An Huang Hai-Cheng Yi |
| author_sort | Yan-Bin Wang |
| collection | DOAJ |
| description | Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences. The improvement mainly comes from using the LMs to extract discriminatory information embedded in the position-specific scoring matrix (PSSM) combined with the PCVM classifier to implement prediction. The proposed method was evaluated on Yeast and Helicobacter pylori datasets with five-fold cross-validation experiments. The experimental results show that the proposed method achieves high average accuracies of 96.37% and 93.48%, respectively, which are much better than other well-known methods. To further evaluate the proposed method, we also compared the proposed method with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the same datasets. The comparison results clearly show that our method is better than the SVM-based method and other existing methods. The promising experimental results show the reliability and effectiveness of the proposed method, which can be a useful decision support tool for protein research. |
| first_indexed | 2024-12-14T18:57:53Z |
| format | Article |
| id | doaj.art-e3c0eaec99e843c99c43b9ee3936a1b0 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-14T18:57:53Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-e3c0eaec99e843c99c43b9ee3936a1b02022-12-21T22:51:02ZengMDPI AGMolecules1420-30492017-08-01228136610.3390/molecules22081366molecules22081366Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSMYan-Bin Wang0Zhu-Hong You1Li-Ping Li2Yu-An Huang3Hai-Cheng Yi4Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, ChinaXinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, ChinaXinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, ChinaDepartment of Computing, Hong Kong Polytechnic University, Hong Kong, ChinaXinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, ChinaProtein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences. The improvement mainly comes from using the LMs to extract discriminatory information embedded in the position-specific scoring matrix (PSSM) combined with the PCVM classifier to implement prediction. The proposed method was evaluated on Yeast and Helicobacter pylori datasets with five-fold cross-validation experiments. The experimental results show that the proposed method achieves high average accuracies of 96.37% and 93.48%, respectively, which are much better than other well-known methods. To further evaluate the proposed method, we also compared the proposed method with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the same datasets. The comparison results clearly show that our method is better than the SVM-based method and other existing methods. The promising experimental results show the reliability and effectiveness of the proposed method, which can be a useful decision support tool for protein research.https://www.mdpi.com/1420-3049/22/8/1366protein-protein interactionsLegendre momentsposition specific scoring matrixprobabilistic classification vector machine |
| spellingShingle | Yan-Bin Wang Zhu-Hong You Li-Ping Li Yu-An Huang Hai-Cheng Yi Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM Molecules protein-protein interactions Legendre moments position specific scoring matrix probabilistic classification vector machine |
| title | Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM |
| title_full | Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM |
| title_fullStr | Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM |
| title_full_unstemmed | Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM |
| title_short | Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM |
| title_sort | detection of interactions between proteins by using legendre moments descriptor to extract discriminatory information embedded in pssm |
| topic | protein-protein interactions Legendre moments position specific scoring matrix probabilistic classification vector machine |
| url | https://www.mdpi.com/1420-3049/22/8/1366 |
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