DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks
Abstract Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the i...
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| Format: | Article |
| Language: | English |
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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/s12859-024-06021-z |
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| author | Guang Yang Yinbo Liu Sijian Wen Wenxi Chen Xiaolei Zhu Yongmei Wang |
| author_facet | Guang Yang Yinbo Liu Sijian Wen Wenxi Chen Xiaolei Zhu Yongmei Wang |
| author_sort | Guang Yang |
| collection | DOAJ |
| description | Abstract Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the issue of feature redundancy. We aim to refine DTI prediction accuracy by eliminating redundant features and capitalizing on the node topological structure to enhance feature extraction. To achieve this, we introduce a PCA-augmented multi-layer heterogeneous graph-based network that concentrates on key features throughout the encoding-decoding phase. Our approach initiates with the construction of a heterogeneous graph from various similarity metrics, which is then encoded via a graph neural network. We concatenate and integrate the resultant representation vectors to merge multi-level information. Subsequently, principal component analysis is applied to distill the most informative features, with the random forest algorithm employed for the final decoding of the integrated data. Our method outperforms six baseline models in terms of accuracy, as demonstrated by extensive experimentation. Comprehensive ablation studies, visualization of results, and in-depth case analyses further validate our framework’s efficacy and interpretability, providing a novel tool for drug discovery that integrates multimodal features. |
| format | Article |
| id | doaj-art-e21a7592be114661a9a0d4f454ad8287 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-e21a7592be114661a9a0d4f454ad82872025-01-19T12:40:54ZengBMCBMC Bioinformatics1471-21052025-01-0126112410.1186/s12859-024-06021-zDTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networksGuang Yang0Yinbo Liu1Sijian Wen2Wenxi Chen3Xiaolei Zhu4Yongmei Wang5School of Information and Artificial Intelligence, Anhui Agricultural UniversitySchool of Information and Artificial Intelligence, Anhui Agricultural UniversitySchool of Information and Artificial Intelligence, Anhui Agricultural UniversitySchool of Information and Artificial Intelligence, Anhui Agricultural UniversitySchool of Information and Artificial Intelligence, Anhui Agricultural UniversitySchool of Information and Artificial Intelligence, Anhui Agricultural UniversityAbstract Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the issue of feature redundancy. We aim to refine DTI prediction accuracy by eliminating redundant features and capitalizing on the node topological structure to enhance feature extraction. To achieve this, we introduce a PCA-augmented multi-layer heterogeneous graph-based network that concentrates on key features throughout the encoding-decoding phase. Our approach initiates with the construction of a heterogeneous graph from various similarity metrics, which is then encoded via a graph neural network. We concatenate and integrate the resultant representation vectors to merge multi-level information. Subsequently, principal component analysis is applied to distill the most informative features, with the random forest algorithm employed for the final decoding of the integrated data. Our method outperforms six baseline models in terms of accuracy, as demonstrated by extensive experimentation. Comprehensive ablation studies, visualization of results, and in-depth case analyses further validate our framework’s efficacy and interpretability, providing a novel tool for drug discovery that integrates multimodal features.https://doi.org/10.1186/s12859-024-06021-zDTIs predictionHeterogeneous graph attention networksFeature concatenationPCARandom forest |
| spellingShingle | Guang Yang Yinbo Liu Sijian Wen Wenxi Chen Xiaolei Zhu Yongmei Wang DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks BMC Bioinformatics DTIs prediction Heterogeneous graph attention networks Feature concatenation PCA Random forest |
| title | DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks |
| title_full | DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks |
| title_fullStr | DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks |
| title_full_unstemmed | DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks |
| title_short | DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks |
| title_sort | dti mhapr optimized drug target interaction prediction via pca enhanced features and heterogeneous graph attention networks |
| topic | DTIs prediction Heterogeneous graph attention networks Feature concatenation PCA Random forest |
| url | https://doi.org/10.1186/s12859-024-06021-z |
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