SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction
Abstract Background A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein function prediction, Protein-Protein Interaction (PPI) Networks have played a crucial role. Uncovering potential function relationships betwe...
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BMC
2025-02-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06059-7 |
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| author | Yansong Wang Yundong Sun Baohui Lin Haotian Zhang Xiaoling Luo Yumeng Liu Xiaopeng Jin Dongjie Zhu |
| author_facet | Yansong Wang Yundong Sun Baohui Lin Haotian Zhang Xiaoling Luo Yumeng Liu Xiaopeng Jin Dongjie Zhu |
| author_sort | Yansong Wang |
| collection | DOAJ |
| description | Abstract Background A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein function prediction, Protein-Protein Interaction (PPI) Networks have played a crucial role. Uncovering potential function relationships between distant proteins within PPI networks is essential for improving the accuracy of protein function prediction. Most current studies attempt to capture these distant relationships by stacking graph network layers, but performance gains diminish as the number of layers increases. Results To further explore the potential functional relationships between multi-hop proteins in PPI networks, this paper proposes SEGT-GO, a Graph Transformer method based on PPI multi-hop neighborhood Serialization and Explainable artificial intelligence for large-scale multispecies protein function prediction. The multi-hop neighborhood serialization maps multi-hop information in the PPI Network into serialized feature embeddings, enabling the Graph Transformer to learn deeper functional features within the PPI Network. Based on game theory, the SHAP eXplainable Artificial Intelligence (XAI) framework optimizes model input and filters out feature noise, enhancing model performance. Conclusions Compared to the advanced network method DeepGraphGO, SEGT-GO achieves more competitive results in standard large-scale datasets and superior results on small ones, validating its ability to extract functional information from deep proteins. Furthermore, SEGT-GO achieves superior results in cross-species learning and prediction of the functions of unseen proteins, further proving the method’s strong generalization. |
| format | Article |
| id | doaj-art-eb95ddbc3d5f468a9233d42c34d077b7 |
| institution | DOAJ |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-eb95ddbc3d5f468a9233d42c34d077b72025-08-20T02:43:13ZengBMCBMC Bioinformatics1471-21052025-02-0126112210.1186/s12859-025-06059-7SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function predictionYansong Wang0Yundong Sun1Baohui Lin2Haotian Zhang3Xiaoling Luo4Yumeng Liu5Xiaopeng Jin6Dongjie Zhu7School of Computer Science and Technology, Harbin Institute of Technology Weihai CampusSchool of Computer Science and Technology, Harbin Institute of Technology Weihai CampusCollege of Big Data and Internet, Shenzhen Technology UniversitySchool of Computer Science and Technology, Harbin Institute of Technology Weihai CampusCollege of Computer Science and Software Engineering, Shenzhen UniversityCollege of Big Data and Internet, Shenzhen Technology UniversityCollege of Big Data and Internet, Shenzhen Technology UniversitySchool of Computer Science and Technology, Harbin Institute of Technology Weihai CampusAbstract Background A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein function prediction, Protein-Protein Interaction (PPI) Networks have played a crucial role. Uncovering potential function relationships between distant proteins within PPI networks is essential for improving the accuracy of protein function prediction. Most current studies attempt to capture these distant relationships by stacking graph network layers, but performance gains diminish as the number of layers increases. Results To further explore the potential functional relationships between multi-hop proteins in PPI networks, this paper proposes SEGT-GO, a Graph Transformer method based on PPI multi-hop neighborhood Serialization and Explainable artificial intelligence for large-scale multispecies protein function prediction. The multi-hop neighborhood serialization maps multi-hop information in the PPI Network into serialized feature embeddings, enabling the Graph Transformer to learn deeper functional features within the PPI Network. Based on game theory, the SHAP eXplainable Artificial Intelligence (XAI) framework optimizes model input and filters out feature noise, enhancing model performance. Conclusions Compared to the advanced network method DeepGraphGO, SEGT-GO achieves more competitive results in standard large-scale datasets and superior results on small ones, validating its ability to extract functional information from deep proteins. Furthermore, SEGT-GO achieves superior results in cross-species learning and prediction of the functions of unseen proteins, further proving the method’s strong generalization.https://doi.org/10.1186/s12859-025-06059-7Protein function predictionGraph transformerPPI networksMulti-hop neighborhood serializationExplainable artificial intelligence |
| spellingShingle | Yansong Wang Yundong Sun Baohui Lin Haotian Zhang Xiaoling Luo Yumeng Liu Xiaopeng Jin Dongjie Zhu SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction BMC Bioinformatics Protein function prediction Graph transformer PPI networks Multi-hop neighborhood serialization Explainable artificial intelligence |
| title | SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction |
| title_full | SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction |
| title_fullStr | SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction |
| title_full_unstemmed | SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction |
| title_short | SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction |
| title_sort | segt go a graph transformer method based on ppi serialization and explanatory artificial intelligence for protein function prediction |
| topic | Protein function prediction Graph transformer PPI networks Multi-hop neighborhood serialization Explainable artificial intelligence |
| url | https://doi.org/10.1186/s12859-025-06059-7 |
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