PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction
Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidenc...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2015/608042 |
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| _version_ | 1832566709455683584 |
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| author | Jianzhuang Yao Hong Guo Xiaohan Yang |
| author_facet | Jianzhuang Yao Hong Guo Xiaohan Yang |
| author_sort | Jianzhuang Yao |
| collection | DOAJ |
| description | Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using an assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. This pipeline will be useful for predicting PPI in nonmodel species. |
| format | Article |
| id | doaj-art-ca8df57da78d444fb9ad773d9a3222e9 |
| institution | Kabale University |
| issn | 2314-436X 2314-4378 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Genomics |
| spelling | doaj-art-ca8df57da78d444fb9ad773d9a3222e92025-02-03T01:03:30ZengWileyInternational Journal of Genomics2314-436X2314-43782015-01-01201510.1155/2015/608042608042PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction PredictionJianzhuang Yao0Hong Guo1Xiaohan Yang2Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USADepartment of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USABiosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USADetermining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using an assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. This pipeline will be useful for predicting PPI in nonmodel species.http://dx.doi.org/10.1155/2015/608042 |
| spellingShingle | Jianzhuang Yao Hong Guo Xiaohan Yang PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction International Journal of Genomics |
| title | PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction |
| title_full | PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction |
| title_fullStr | PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction |
| title_full_unstemmed | PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction |
| title_short | PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction |
| title_sort | ppcm combing multiple classifiers to improve protein protein interaction prediction |
| url | http://dx.doi.org/10.1155/2015/608042 |
| work_keys_str_mv | AT jianzhuangyao ppcmcombingmultipleclassifierstoimproveproteinproteininteractionprediction AT hongguo ppcmcombingmultipleclassifierstoimproveproteinproteininteractionprediction AT xiaohanyang ppcmcombingmultipleclassifierstoimproveproteinproteininteractionprediction |