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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.06682 |
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| _version_ | 1866929552655122432 |
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| author | Ma, Tengfei Chen, Yujie Tao, Wen Zheng, Dashun Lin, Xuan Pang, Patrick Cheong-lao Liu, Yiping Wang, Yijun Wang, Longyue Song, Bosheng Zeng, Xiangxiang Yu, Philip S. |
| author_facet | Ma, Tengfei Chen, Yujie Tao, Wen Zheng, Dashun Lin, Xuan Pang, Patrick Cheong-lao Liu, Yiping Wang, Yijun Wang, Longyue Song, Bosheng Zeng, Xiangxiang Yu, Philip S. |
| contents | Molecular interaction prediction plays a crucial role in forecasting unknown interactions between molecules, such as drug-target interaction (DTI) and drug-drug interaction (DDI), which are essential in the field of drug discovery and therapeutics. Although previous prediction methods have yielded promising results by leveraging the rich semantics and topological structure of biomedical knowledge graphs (KGs), they have primarily focused on enhancing predictive performance without addressing the presence of inevitable noise and inconsistent semantics. This limitation has hindered the advancement of KG-based prediction methods. To address this limitation, we propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner, providing a general module for extracting task-relevant interactions. To enhance the reliability of the refined structure, BioKDN maintains consistent and robust semantics by smoothing relations around the target interaction. By maximizing the mutual information between reliable structure and smoothed relations, BioKDN emphasizes informative semantics to enable precise predictions. Experimental results on real-world datasets show that BioKDN surpasses state-of-the-art models in DTI and DDI prediction tasks, confirming the effectiveness and robustness of BioKDN in denoising unreliable interactions within contaminated KGs |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06682 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction Ma, Tengfei Chen, Yujie Tao, Wen Zheng, Dashun Lin, Xuan Pang, Patrick Cheong-lao Liu, Yiping Wang, Yijun Wang, Longyue Song, Bosheng Zeng, Xiangxiang Yu, Philip S. Artificial Intelligence Machine Learning Molecular interaction prediction plays a crucial role in forecasting unknown interactions between molecules, such as drug-target interaction (DTI) and drug-drug interaction (DDI), which are essential in the field of drug discovery and therapeutics. Although previous prediction methods have yielded promising results by leveraging the rich semantics and topological structure of biomedical knowledge graphs (KGs), they have primarily focused on enhancing predictive performance without addressing the presence of inevitable noise and inconsistent semantics. This limitation has hindered the advancement of KG-based prediction methods. To address this limitation, we propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner, providing a general module for extracting task-relevant interactions. To enhance the reliability of the refined structure, BioKDN maintains consistent and robust semantics by smoothing relations around the target interaction. By maximizing the mutual information between reliable structure and smoothed relations, BioKDN emphasizes informative semantics to enable precise predictions. Experimental results on real-world datasets show that BioKDN surpasses state-of-the-art models in DTI and DDI prediction tasks, confirming the effectiveness and robustness of BioKDN in denoising unreliable interactions within contaminated KGs |
| title | Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2312.06682 |