Yiqing Luo , Lin Liu , Yaxin Fu , Yi Deng , Lin Tang
{"title":"基于方向约束扩散的单细胞微扰响应预测Schrödinger桥","authors":"Yiqing Luo , Lin Liu , Yaxin Fu , Yi Deng , Lin Tang","doi":"10.1016/j.jbi.2025.104915","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>Predicting transcriptional responses to external perturbations at the single-cell level is essential for understanding gene regulatory networks, drug discovery, and personalized interventions. The exponential increase in perturbation conditions creates data sparsity, making it difficult to capture dynamic responses and necessitating computational modeling.</div></div><div><h3>Methods</h3><div>We present Direction-Constrained Diffusion Schrödinger Bridge (DC-DSB), a generative framework that learns probabilistic trajectories between unperturbed and post-perturbation distributions by minimizing path-space KL divergence. To enhance conditional control, DC-DSB integrates hierarchical representations derived from experimental variables and biological prior knowledge. We further introduce a direction-constrained conditioning strategy that injects condition signals along the biologically relevant perturbation trajectory, thereby improving modeling quality and training stability.</div></div><div><h3>Results</h3><div>DC-DSB improves expression prediction accuracy and generalization to unseen combinations over baselines. By modeling dynamic expression trajectories and co-expression structures under perturbation, DC-DSB enables the discovery of synergistic and antagonistic gene interactions and supports the progressive reconstruction of regulatory pathways.</div></div><div><h3>Conclusion</h3><div>DC-DSB provides a biologically consistent and generalizable framework for single-cell perturbation modeling. Its trajectory-based and condition-aware architecture overcomes the limitations of static mappings and facilitates downstream analyses in gene regulation and drug discovery.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104915"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Single-Cell perturbation response based on Direction-Constrained diffusion Schrödinger Bridge\",\"authors\":\"Yiqing Luo , Lin Liu , Yaxin Fu , Yi Deng , Lin Tang\",\"doi\":\"10.1016/j.jbi.2025.104915\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>Predicting transcriptional responses to external perturbations at the single-cell level is essential for understanding gene regulatory networks, drug discovery, and personalized interventions. The exponential increase in perturbation conditions creates data sparsity, making it difficult to capture dynamic responses and necessitating computational modeling.</div></div><div><h3>Methods</h3><div>We present Direction-Constrained Diffusion Schrödinger Bridge (DC-DSB), a generative framework that learns probabilistic trajectories between unperturbed and post-perturbation distributions by minimizing path-space KL divergence. To enhance conditional control, DC-DSB integrates hierarchical representations derived from experimental variables and biological prior knowledge. We further introduce a direction-constrained conditioning strategy that injects condition signals along the biologically relevant perturbation trajectory, thereby improving modeling quality and training stability.</div></div><div><h3>Results</h3><div>DC-DSB improves expression prediction accuracy and generalization to unseen combinations over baselines. By modeling dynamic expression trajectories and co-expression structures under perturbation, DC-DSB enables the discovery of synergistic and antagonistic gene interactions and supports the progressive reconstruction of regulatory pathways.</div></div><div><h3>Conclusion</h3><div>DC-DSB provides a biologically consistent and generalizable framework for single-cell perturbation modeling. Its trajectory-based and condition-aware architecture overcomes the limitations of static mappings and facilitates downstream analyses in gene regulation and drug discovery.</div></div>\",\"PeriodicalId\":15263,\"journal\":{\"name\":\"Journal of Biomedical Informatics\",\"volume\":\"170 \",\"pages\":\"Article 104915\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Biomedical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1532046425001443\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biomedical Informatics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1532046425001443","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Prediction of Single-Cell perturbation response based on Direction-Constrained diffusion Schrödinger Bridge
Objective
Predicting transcriptional responses to external perturbations at the single-cell level is essential for understanding gene regulatory networks, drug discovery, and personalized interventions. The exponential increase in perturbation conditions creates data sparsity, making it difficult to capture dynamic responses and necessitating computational modeling.
Methods
We present Direction-Constrained Diffusion Schrödinger Bridge (DC-DSB), a generative framework that learns probabilistic trajectories between unperturbed and post-perturbation distributions by minimizing path-space KL divergence. To enhance conditional control, DC-DSB integrates hierarchical representations derived from experimental variables and biological prior knowledge. We further introduce a direction-constrained conditioning strategy that injects condition signals along the biologically relevant perturbation trajectory, thereby improving modeling quality and training stability.
Results
DC-DSB improves expression prediction accuracy and generalization to unseen combinations over baselines. By modeling dynamic expression trajectories and co-expression structures under perturbation, DC-DSB enables the discovery of synergistic and antagonistic gene interactions and supports the progressive reconstruction of regulatory pathways.
Conclusion
DC-DSB provides a biologically consistent and generalizable framework for single-cell perturbation modeling. Its trajectory-based and condition-aware architecture overcomes the limitations of static mappings and facilitates downstream analyses in gene regulation and drug discovery.
期刊介绍:
The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.