Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms
IntroductionUnderstanding the mechanisms of drug-induced bone remodeling is critical for optimizing therapeutic interventions and minimizing adverse effects in bone health management. Bone remodeling is a highly dynamic process that involves the intricate interplay between osteoblasts, osteoclasts,...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1564157/full |
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| author | Li Qinsheng Li Ming Li Yuening Zhao Xiufeng |
| author_facet | Li Qinsheng Li Ming Li Yuening Zhao Xiufeng |
| author_sort | Li Qinsheng |
| collection | DOAJ |
| description | IntroductionUnderstanding the mechanisms of drug-induced bone remodeling is critical for optimizing therapeutic interventions and minimizing adverse effects in bone health management. Bone remodeling is a highly dynamic process that involves the intricate interplay between osteoblasts, osteoclasts, and osteocytes, regulated by a complex network of signaling pathways and molecular interactions. Traditional experimental and computational approaches often fail to capture this dynamic and multi-scale nature, particularly when influenced by pharmacological agents, which can have both therapeutic and adverse effects.MethodsIn this work, we present a novel deep learning-based framework for action recognition, specifically designed to analyze drug-induced bone remodeling mechanisms. Our framework leverages graph neural networks (GNNs) to model the spatial and temporal dependencies of multi-scale biological data, combined with a dynamic signal propagation model to identify key molecular interactions driving bone remodeling. A predictive pharmacological interaction model is integrated to quantify drug-target interactions, assess their systemic impacts, and simulate off-target effects. This approach also evaluates combinatorial drug effects, offering insights into the synergistic or antagonistic behaviors of multiple agents.ResultsBy incorporating these features, our method provides a comprehensive view of drug-induced changes, enabling accurate prediction of their effects on bone formation and resorption pathways.DiscussionExperimental results highlight the model’s potential to advance precision medicine, enabling the development of more effective and safer therapeutic strategies for managing bone health. |
| format | Article |
| id | doaj-art-c022ac3f0801438886e35e1bbfd247cc |
| institution | OA Journals |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-c022ac3f0801438886e35e1bbfd247cc2025-08-20T02:01:24ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-05-011610.3389/fphar.2025.15641571564157Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanismsLi Qinsheng0Li Ming1Li Yuening2Zhao Xiufeng3Physical Education Department of Taishan University, Taian, ChinaSchool of Physical education, Linyi University, Linyi, ChinaSports Training College, Wuhan sports University, Wuhan, ChinaPhysical Education Department of Taishan University, Taian, ChinaIntroductionUnderstanding the mechanisms of drug-induced bone remodeling is critical for optimizing therapeutic interventions and minimizing adverse effects in bone health management. Bone remodeling is a highly dynamic process that involves the intricate interplay between osteoblasts, osteoclasts, and osteocytes, regulated by a complex network of signaling pathways and molecular interactions. Traditional experimental and computational approaches often fail to capture this dynamic and multi-scale nature, particularly when influenced by pharmacological agents, which can have both therapeutic and adverse effects.MethodsIn this work, we present a novel deep learning-based framework for action recognition, specifically designed to analyze drug-induced bone remodeling mechanisms. Our framework leverages graph neural networks (GNNs) to model the spatial and temporal dependencies of multi-scale biological data, combined with a dynamic signal propagation model to identify key molecular interactions driving bone remodeling. A predictive pharmacological interaction model is integrated to quantify drug-target interactions, assess their systemic impacts, and simulate off-target effects. This approach also evaluates combinatorial drug effects, offering insights into the synergistic or antagonistic behaviors of multiple agents.ResultsBy incorporating these features, our method provides a comprehensive view of drug-induced changes, enabling accurate prediction of their effects on bone formation and resorption pathways.DiscussionExperimental results highlight the model’s potential to advance precision medicine, enabling the development of more effective and safer therapeutic strategies for managing bone health.https://www.frontiersin.org/articles/10.3389/fphar.2025.1564157/fullbone remodelingdeep learningpharmacological mechanismsdrug-target interactiongraph neural networks |
| spellingShingle | Li Qinsheng Li Ming Li Yuening Zhao Xiufeng Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms Frontiers in Pharmacology bone remodeling deep learning pharmacological mechanisms drug-target interaction graph neural networks |
| title | Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms |
| title_full | Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms |
| title_fullStr | Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms |
| title_full_unstemmed | Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms |
| title_short | Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms |
| title_sort | deep learning based action recognition for analyzing drug induced bone remodeling mechanisms |
| topic | bone remodeling deep learning pharmacological mechanisms drug-target interaction graph neural networks |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1564157/full |
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