Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information
Abstract Background Prediction of protein–protein interactions (PPIs) is fundamental for identifying drug targets and understanding cellular processes. The rapid growth of PPI studies necessitates the development of efficient and accurate tools for automated prediction of PPIs. In recent years, seve...
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BMC
2025-08-01
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| Series: | BMC Biology |
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| Online Access: | https://doi.org/10.1186/s12915-025-02359-9 |
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| author | Tao Tang Taiguang Shen Jing Jiang Weizhuo Li Peng Wang Sisi Yuan Xiaofeng Cao Yuansheng Liu |
| author_facet | Tao Tang Taiguang Shen Jing Jiang Weizhuo Li Peng Wang Sisi Yuan Xiaofeng Cao Yuansheng Liu |
| author_sort | Tao Tang |
| collection | DOAJ |
| description | Abstract Background Prediction of protein–protein interactions (PPIs) is fundamental for identifying drug targets and understanding cellular processes. The rapid growth of PPI studies necessitates the development of efficient and accurate tools for automated prediction of PPIs. In recent years, several robust deep learning models have been developed for PPI prediction and have found widespread application in proteomics research. Despite these advancements, current computational tools still face limitations in modeling both the pairwise interactions and the hierarchical relationships between proteins. Results We present HI-PPI, a novel deep learning method that integrates hierarchical representation of PPI network and interaction-specific learning for protein–protein interaction prediction. HI-PPI extracts the hierarchical information by embedding structural and relational information into hyperbolic space. A gated interaction network is then employed to extract pairwise features for interaction prediction. Experiments on multiple benchmark datasets demonstrate that HI-PPI outperforms the state-of-the-art methods; HI-PPI improves Micro-F1 scores by 2.62%–7.09% over the second-best method. Moreover, HI-PPI offers explicit interpretability of the hierarchical organization within the PPI network. The distance between the origin and the hyperbolic embedding computed by HI-PPI naturally reflects the hierarchical level of proteins. Conclusions Overall, the proposed HI-PPI effectively addresses the limitations of existing PPI prediction methods. By leveraging the hierarchical structure of PPI network, HI-PPI significantly enhances the accuracy and robustness of PPI predictions. |
| format | Article |
| id | doaj-art-df10a02ea4214cbf8e22c4a6b456fe04 |
| institution | Kabale University |
| issn | 1741-7007 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Biology |
| spelling | doaj-art-df10a02ea4214cbf8e22c4a6b456fe042025-08-20T04:03:07ZengBMCBMC Biology1741-70072025-08-0123111410.1186/s12915-025-02359-9Prediction of protein–protein interaction based on interaction-specific learning and hierarchical informationTao Tang0Taiguang Shen1Jing Jiang2Weizhuo Li3Peng Wang4Sisi Yuan5Xiaofeng Cao6Yuansheng Liu7School of Modern Posts, Nanjing University of Posts and TelecommunicationsSchool of Modern Posts, Nanjing University of Posts and TelecommunicationsCollege of Computer Science and Electronic Engineering, Hunan UniversitySchool of Modern Posts, Nanjing University of Posts and TelecommunicationsSchool of Electronic Information, Hunan First Normal UniversityDepartment of Bioinformatics and Genomics, University of North Carolina at CharlotteSchool of Computer Science and Technology, Tongji UniversityCollege of Computer Science and Electronic Engineering, Hunan UniversityAbstract Background Prediction of protein–protein interactions (PPIs) is fundamental for identifying drug targets and understanding cellular processes. The rapid growth of PPI studies necessitates the development of efficient and accurate tools for automated prediction of PPIs. In recent years, several robust deep learning models have been developed for PPI prediction and have found widespread application in proteomics research. Despite these advancements, current computational tools still face limitations in modeling both the pairwise interactions and the hierarchical relationships between proteins. Results We present HI-PPI, a novel deep learning method that integrates hierarchical representation of PPI network and interaction-specific learning for protein–protein interaction prediction. HI-PPI extracts the hierarchical information by embedding structural and relational information into hyperbolic space. A gated interaction network is then employed to extract pairwise features for interaction prediction. Experiments on multiple benchmark datasets demonstrate that HI-PPI outperforms the state-of-the-art methods; HI-PPI improves Micro-F1 scores by 2.62%–7.09% over the second-best method. Moreover, HI-PPI offers explicit interpretability of the hierarchical organization within the PPI network. The distance between the origin and the hyperbolic embedding computed by HI-PPI naturally reflects the hierarchical level of proteins. Conclusions Overall, the proposed HI-PPI effectively addresses the limitations of existing PPI prediction methods. By leveraging the hierarchical structure of PPI network, HI-PPI significantly enhances the accuracy and robustness of PPI predictions.https://doi.org/10.1186/s12915-025-02359-9Protein–protein interactionsDeep learningInteraction-specific learningHyperbolic space |
| spellingShingle | Tao Tang Taiguang Shen Jing Jiang Weizhuo Li Peng Wang Sisi Yuan Xiaofeng Cao Yuansheng Liu Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information BMC Biology Protein–protein interactions Deep learning Interaction-specific learning Hyperbolic space |
| title | Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information |
| title_full | Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information |
| title_fullStr | Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information |
| title_full_unstemmed | Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information |
| title_short | Prediction of protein–protein interaction based on interaction-specific learning and hierarchical information |
| title_sort | prediction of protein protein interaction based on interaction specific learning and hierarchical information |
| topic | Protein–protein interactions Deep learning Interaction-specific learning Hyperbolic space |
| url | https://doi.org/10.1186/s12915-025-02359-9 |
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