Annual Review of Biomedical Data Science最新文献

Strategies for Creating Robust Patient Groups to Study Diverse Conditions with Electronic Health Records. 创建健全的患者群体以研究电子健康记录的不同条件的策略。

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-08 DOI: 10.1146/annurev-biodatasci-020722-114525

Grace D Ramey, Hannah Takasuka, John A Capra

引用次数: 0

Mapping, Modeling, and Reprogramming Cell-Fate Decision-Making Systems. 绘图、建模和重编程细胞命运决策系统。

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-05-01 DOI: 10.1146/annurev-biodatasci-101424-121439

Lucy Ham, Taylor E Woodward, Megan A Coomer, Michael P H Stumpf

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The TITAN-X Platform Integrates Big Data, Artificial Intelligence, Bioinformatics, and Advanced Computational Modeling to Understand Immune Responses and Develop the Next Wave of Precision Medicines. TITAN-X平台集成了大数据、人工智能、生物信息学和先进的计算建模，以了解免疫反应并开发下一波精准医疗。

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-16 DOI: 10.1146/annurev-biodatasci-103123-094804

Ryan Baker, Josep Bassaganya-Riera, Nuria Tubau-Juni, Andrew J Leber, Raquel Hontecillas

{"title":"The TITAN-X Platform Integrates Big Data, Artificial Intelligence, Bioinformatics, and Advanced Computational Modeling to Understand Immune Responses and Develop the Next Wave of Precision Medicines.","authors":"Ryan Baker, Josep Bassaganya-Riera, Nuria Tubau-Juni, Andrew J Leber, Raquel Hontecillas","doi":"10.1146/annurev-biodatasci-103123-094804","DOIUrl":"10.1146/annurev-biodatasci-103123-094804","url":null,"abstract":"The TITAN-X Precision Medicine Platform was engineered to rapidly, fully, and efficiently utilize large-scale immunology datasets, including public data, in drug discovery and development. TITAN-X integrates big data with artificial intelligence (AI), bioinformatics, and advanced computational modeling to seamlessly transition from early target discovery to clinical testing of new therapeutics, developing biomarker-driven precision medicines tailored to specific patient populations. We illustrate the capabilities of TITAN-X through four case studies, demonstrating its use in computationally driven target discovery; characterization of novel immunometabolic mechanisms in infectious, inflammatory, and autoimmune diseases; and identification of biomarker signatures for patient stratification in clinical trials designed to maximize therapeutic efficacy and safety. Data-driven and AI-powered approaches like TITAN-X are enhancing the pace of drug development, reducing costs, tailoring treatments, and increasing the probability of success in clinical trials.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"447-469"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Meta-Analysis and Federated Learning over Decentralized Distributed Research Networks. 分散式分布式研究网络的元分析和联邦学习。

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 DOI: 10.1146/annurev-biodatasci-103123-094441

Yiwen Lu, Bingyu Zhang, Jiayi Tong, Yong Chen

{"title":"Meta-Analysis and Federated Learning over Decentralized Distributed Research Networks.","authors":"Yiwen Lu, Bingyu Zhang, Jiayi Tong, Yong Chen","doi":"10.1146/annurev-biodatasci-103123-094441","DOIUrl":"10.1146/annurev-biodatasci-103123-094441","url":null,"abstract":"Distributed research networks have transformed modern clinical research by enabling large-scale, multi-institutional collaborations while maintaining patient privacy. Two prominent methodologies within these frameworks-meta-analysis and federated learning-address the challenges of synthesizing evidence from decentralized data. Meta-analysis aggregates study-level results to provide robust, interpretable estimates, making it a cornerstone of evidence synthesis for association studies. Federated learning complements this by enabling complex downstream tasks, such as predictive modeling and counterfactual inference, while preserving data privacy through privacy-preserving distributed algorithms. Federated learning facilitates communication-efficient computation and adapts seamlessly to heterogeneous datasets across diverse institutions. This review emphasizes the complementary strengths of federated learning's scalability, flexibility, and readiness for implementation alongside meta-analysis's robust frameworks for evidence synthesis and aggregation in clinical research. Integrations of synthetic data, artificial intelligence (AI)-enhanced harmonization, and hybrid human-AI frameworks are proposed as future directions, promising to further advance both methodologies and enhance their combined impact on privacy-conscious, data-driven healthcare research.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"8 1","pages":"405-421"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144822752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Foundation Models for Translational Cancer Biology. 转化癌症生物学基础模型。

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-01-29 DOI: 10.1146/annurev-biodatasci-103123-095633

Kevin K Tsang, Sophia Kivelson, Jose M Acitores Cortina, Aditi Kuchi, Jacob S Berkowitz, Hongyu Liu, Apoorva Srinivasan, Nadine A Friedrich, Yasaman Fatapour, Nicholas P Tatonetti

{"title":"Foundation Models for Translational Cancer Biology.","authors":"Kevin K Tsang, Sophia Kivelson, Jose M Acitores Cortina, Aditi Kuchi, Jacob S Berkowitz, Hongyu Liu, Apoorva Srinivasan, Nadine A Friedrich, Yasaman Fatapour, Nicholas P Tatonetti","doi":"10.1146/annurev-biodatasci-103123-095633","DOIUrl":"10.1146/annurev-biodatasci-103123-095633","url":null,"abstract":"Cancer remains a leading cause of death globally. The complexity and diversity of cancer-related datasets across different specialties pose challenges in refining precision medicine for oncology. Foundation models offer a promising solution. Trained on vast amounts of data, these models develop a broad understanding across a wide range of tasks. We examine the role of foundation models in domains relevant to cancer research, including natural language processing, computer vision, molecular biology, and cheminformatics. Through a review of state-of-the-art methods, we explore how these models have already advanced translational cancer research goals such as precision tumor classification and artificial intelligence-assisted surgery. We also discuss prospective advances in areas like early tumor detection, personalized cancer treatment, and drug discovery. This review provides researchers with a curated set of resources and methodologies, offers practitioners a deeper understanding of how these models enhance cancer care, and points to opportunities for future applications of foundation models in cancer research.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"51-80"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Clinical Text Generation: Are We There Yet? 临床文本生成：我们做到了吗？

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-03-18 DOI: 10.1146/annurev-biodatasci-103123-095202

Nicolas Hiebel, Olivier Ferret, Karën Fort, Aurélie Névéol

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Conditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse Representations. 合成表格数据的条件生成模型：精准医疗和多样化表示的应用。

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-01-14 DOI: 10.1146/annurev-biodatasci-103123-094844

Kara Liu, Russ B Altman

{"title":"Conditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse Representations.","authors":"Kara Liu, Russ B Altman","doi":"10.1146/annurev-biodatasci-103123-094844","DOIUrl":"10.1146/annurev-biodatasci-103123-094844","url":null,"abstract":"Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured clinical trial data, are rich sources of information with the potential to advance precision medicine and optimize patient care. However, real-world medical datasets have limited patient diversity and cannot simulate hypothetical outcomes, both of which are necessary for equitable and effective medical research. Fueled by recent advancements in machine learning, generative models offer a promising solution to these data limitations by generating enhanced synthetic data. This review highlights the potential of conditional generative models (CGMs) to create patient-specific synthetic data for a variety of precision medicine applications. We survey CGM approaches that tackle two medical applications: correcting for data representation biases and simulating digital health twins. We additionally explore how the surveyed methods handle modeling tabular medical data and briefly discuss evaluation criteria. Finally, we summarize the technical, medical, and ethical challenges that must be addressed before CGMs can be effectively and safely deployed in the medical field.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"21-49"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Integrative Data Science in Drug Safety Research: Experiences, Challenges, and Perspectives. 药物安全研究中的综合数据科学：经验、挑战和展望。

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-01 DOI: 10.1146/annurev-biodatasci-103123-095506

Ferran Sanz

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Integrating Artificial Intelligence in Dermatological Cancer Screening and Diagnosis: Efficacy, Challenges, and Future Directions. 将人工智能应用于皮肤病癌症筛查和诊断：疗效、挑战和未来方向。

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-05-01 DOI: 10.1146/annurev-biodatasci-103123-094521

Vivian Utti, Vasiliki Bikia, Ank A Agarwal, Roxana Daneshjou

{"title":"Integrating Artificial Intelligence in Dermatological Cancer Screening and Diagnosis: Efficacy, Challenges, and Future Directions.","authors":"Vivian Utti, Vasiliki Bikia, Ank A Agarwal, Roxana Daneshjou","doi":"10.1146/annurev-biodatasci-103123-094521","DOIUrl":"10.1146/annurev-biodatasci-103123-094521","url":null,"abstract":"Skin cancer is the most common cancer in the United States, with incidence rates continuing to rise both nationally and globally, posing significant health and economic burdens. These challenges are compounded by shortages in dermatological care and barriers to insurance access. To address these gaps, artificial intelligence (AI) and deep learning technologies offer promising solutions, enhancing skin cancer screening and diagnosis. AI has the potential to improve diagnostic accuracy and expand access to care, but significant challenges restrict its deployment. These challenges include clinical validation, algorithmic bias, regulatory oversight, and patient acceptance. Ethical concerns, such as disparities in access and fairness of AI algorithms, also require attention. In this review, we explore these limitations and outline future directions, including advancements in teledermatology and vision-language models (VLMs). Future research should focus on improving VLM reliability and interpretability and developing systems capable of integrating clinical context with dermatological images in a way that assists, rather than replaces, clinicians in making more accurate, timely diagnoses.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"591-603"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Network-Based Approaches for Drug Target Identification. 基于网络的药物靶标识别方法。

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Annual Review of Biomedical Data Science Pub Date : 2025-08-01 Epub Date: 2025-04-16 DOI: 10.1146/annurev-biodatasci-101424-120950

Thodoris Koutsandreas, Kalliopi Tsafou, Heiko Horn, Ian Barrett, Evangelia Petsalaki

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