Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction
Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2010
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| Online Access: | http://eprints.uthm.edu.my/7880/ http://eprints.uthm.edu.my/7880/1/J7534_a3be278a77226cd722c59986e8c53d51.pdf |
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| author | Roslan, Rosfuzah M. Othman, Razib A. Shah, Zuraini Kasim, Shahreen Asmuni, Hishammuddin Talib, Jumail Hassan, Rohayanti Zakaria, Zalmiyah |
| author_facet | Roslan, Rosfuzah M. Othman, Razib A. Shah, Zuraini Kasim, Shahreen Asmuni, Hishammuddin Talib, Jumail Hassan, Rohayanti Zakaria, Zalmiyah |
| author_sort | Roslan, Rosfuzah |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
Rosfuzah Roslan 1, Razib M Othman, Zuraini A Shah, Shahreen Kasim, Hishammuddin Asmuni, Jumail Taliba, Rohayanti Hassan, Zalmiyah Zakaria |
| first_indexed | 2025-11-15T20:22:55Z |
| format | Article |
| id | uthm-7880 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:22:55Z |
| publishDate | 2010 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-78802022-10-17T06:20:11Z http://eprints.uthm.edu.my/7880/ Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction Roslan, Rosfuzah M. Othman, Razib A. Shah, Zuraini Kasim, Shahreen Asmuni, Hishammuddin Talib, Jumail Hassan, Rohayanti Zakaria, Zalmiyah T Technology (General) Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics. Rosfuzah Roslan 1, Razib M Othman, Zuraini A Shah, Shahreen Kasim, Hishammuddin Asmuni, Jumail Taliba, Rohayanti Hassan, Zalmiyah Zakaria Elsevier 2010 Article PeerReviewed text en http://eprints.uthm.edu.my/7880/1/J7534_a3be278a77226cd722c59986e8c53d51.pdf Roslan, Rosfuzah and M. Othman, Razib and A. Shah, Zuraini and Kasim, Shahreen and Asmuni, Hishammuddin and Talib, Jumail and Hassan, Rohayanti and Zakaria, Zalmiyah (2010) Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction. Computers Biology Medicine, 6 (40). pp. 555-564. https://doi.org/10.1016/j.compbiomed.2010.03.009 |
| spellingShingle | T Technology (General) Roslan, Rosfuzah M. Othman, Razib A. Shah, Zuraini Kasim, Shahreen Asmuni, Hishammuddin Talib, Jumail Hassan, Rohayanti Zakaria, Zalmiyah Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction |
| title | Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction |
| title_full | Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction |
| title_fullStr | Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction |
| title_full_unstemmed | Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction |
| title_short | Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction |
| title_sort | utilizing shared interacting domain patterns and gene ontology information to improve protein-protein interaction prediction |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/7880/ http://eprints.uthm.edu.my/7880/ http://eprints.uthm.edu.my/7880/1/J7534_a3be278a77226cd722c59986e8c53d51.pdf |