MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction
Abstract Background Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, w...
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BMC
2025-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-05987-0 |
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| author | Dhekra Saeed Huanlai Xing Barakat AlBadani Li Feng Raeed Al-Sabri Monir Abdullah Amir Rehman |
| author_facet | Dhekra Saeed Huanlai Xing Barakat AlBadani Li Feng Raeed Al-Sabri Monir Abdullah Amir Rehman |
| author_sort | Dhekra Saeed |
| collection | DOAJ |
| description | Abstract Background Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, while promising, frequently neglect the critical role of atomic interactions and fail to integrate drug fingerprints with SMILES for comprehensive molecular graph construction. Results We introduce multimodal multi-channel graph attention network with adaptive fusion (MGATAF), a framework designed to enhance drug response predictions by capturing both local and global interactions among graph nodes. MGATAF improves drug representation by integrating SMILES and fingerprints, resulting in more precise predictions of drug effects. The methodology involves constructing multimodal molecular graphs, employing multi-channel graph attention networks to capture diverse interactions, and using adaptive fusion to integrate these interactions at multiple abstraction levels. Empirical results demonstrate MGATAF’s superior performance compared to traditional and other graph-based techniques. For example, on the GDSC dataset, MGATAF achieved a 5.12% improvement in the Pearson correlation coefficient (PCC), reaching 0.9312 with an RMSE of 0.0225. Similarly, in new cell-line tests, MGATAF outperformed baselines with a PCC of 0.8536 and an RMSE of 0.0321 on the GDSC dataset, and a PCC of 0.7364 with an RMSE of 0.0531 on the CCLE dataset. Conclusions MGATAF significantly advances drug response prediction by effectively integrating multiple molecular data types and capturing complex interactions. This framework enhances prediction accuracy and offers a robust tool for personalized medicine, potentially leading to more effective and safer treatments for patients. Future research can expand on this work by exploring additional data modalities and refining the adaptive fusion mechanisms. |
| format | Article |
| id | doaj-art-66b2df18210b44548ca321c747d85925 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-66b2df18210b44548ca321c747d859252025-01-19T12:40:55ZengBMCBMC Bioinformatics1471-21052025-01-0126112410.1186/s12859-024-05987-0MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response predictionDhekra Saeed0Huanlai Xing1Barakat AlBadani2Li Feng3Raeed Al-Sabri4Monir Abdullah5Amir Rehman6School of Computing and Artificial Intelligence, Southwest Jiaotong UniversitySchool of Computing and Artificial Intelligence, Southwest Jiaotong UniversitySchool of Computer Science and Engineering, Central South UniversitySchool of Computing and Artificial Intelligence, Southwest Jiaotong UniversityFaculty of Computer Sciences and Information Systems, Thamar UniversityCollege of Computing and Information Technology, University of BishaSchool of Computing and Artificial Intelligence, Southwest Jiaotong UniversityAbstract Background Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, while promising, frequently neglect the critical role of atomic interactions and fail to integrate drug fingerprints with SMILES for comprehensive molecular graph construction. Results We introduce multimodal multi-channel graph attention network with adaptive fusion (MGATAF), a framework designed to enhance drug response predictions by capturing both local and global interactions among graph nodes. MGATAF improves drug representation by integrating SMILES and fingerprints, resulting in more precise predictions of drug effects. The methodology involves constructing multimodal molecular graphs, employing multi-channel graph attention networks to capture diverse interactions, and using adaptive fusion to integrate these interactions at multiple abstraction levels. Empirical results demonstrate MGATAF’s superior performance compared to traditional and other graph-based techniques. For example, on the GDSC dataset, MGATAF achieved a 5.12% improvement in the Pearson correlation coefficient (PCC), reaching 0.9312 with an RMSE of 0.0225. Similarly, in new cell-line tests, MGATAF outperformed baselines with a PCC of 0.8536 and an RMSE of 0.0321 on the GDSC dataset, and a PCC of 0.7364 with an RMSE of 0.0531 on the CCLE dataset. Conclusions MGATAF significantly advances drug response prediction by effectively integrating multiple molecular data types and capturing complex interactions. This framework enhances prediction accuracy and offers a robust tool for personalized medicine, potentially leading to more effective and safer treatments for patients. Future research can expand on this work by exploring additional data modalities and refining the adaptive fusion mechanisms.https://doi.org/10.1186/s12859-024-05987-0Graph neural networkDrug response predictionPrecision medicineBioinformatics |
| spellingShingle | Dhekra Saeed Huanlai Xing Barakat AlBadani Li Feng Raeed Al-Sabri Monir Abdullah Amir Rehman MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction BMC Bioinformatics Graph neural network Drug response prediction Precision medicine Bioinformatics |
| title | MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction |
| title_full | MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction |
| title_fullStr | MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction |
| title_full_unstemmed | MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction |
| title_short | MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction |
| title_sort | mgataf multi channel graph attention network with adaptive fusion for cancer drug response prediction |
| topic | Graph neural network Drug response prediction Precision medicine Bioinformatics |
| url | https://doi.org/10.1186/s12859-024-05987-0 |
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