发布求助
文献互助 智能选刊 最新文献

Annual Review of Biomedical Data Science最新文献

筛选
英文 中文
The Case for Greener Biomedical Data Science and How to Get There. 绿色生物医学数据科学的案例和如何实现。
IF 8.1
Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-04-28 DOI: 10.1146/annurev-biodatasci-092724-015338
Gabrielle Samuel, Loïc Lannelongue
{"title":"The Case for Greener Biomedical Data Science and How to Get There.","authors":"Gabrielle Samuel, Loïc Lannelongue","doi":"10.1146/annurev-biodatasci-092724-015338","DOIUrl":"10.1146/annurev-biodatasci-092724-015338","url":null,"abstract":"<p><p>Biomedical data science holds immense promise for improving human health. However, the field carries an environmental cost stemming from the energy- and resource-intensive nature of its computational infrastructure. This article provides a comprehensive review of the sector's nascent efforts to address its environmental harms from technical, ethical, sociological, and regulatory perspectives. We identify a growing consensus on the need for action and trace the slow progress of institutionalizing mitigation strategies. To accelerate this process, we propose learning from the historical institutionalization of animal research ethics, building on key lessons associated with legitimization and implementation. Finally, we outline persistent challenges associated with a rebound paradox, as well as the need to consider context-dependent issues of global equity and justice to ensure a responsible and sustainable future for the field.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"213-237"},"PeriodicalIF":8.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783302","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}
引用次数: 0
Clinical Knowledge Representation in Data Science. 数据科学中的临床知识表示。
IF 8.1
Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-04-28 DOI: 10.1146/annurev-biodatasci-092724-050810
Tram Anh Nguyen, Wendy Su, Ananya Rajagopalan, Nia Abdurezak, Oresta S I Hewryk, Joseph D Romano
{"title":"Clinical Knowledge Representation in Data Science.","authors":"Tram Anh Nguyen, Wendy Su, Ananya Rajagopalan, Nia Abdurezak, Oresta S I Hewryk, Joseph D Romano","doi":"10.1146/annurev-biodatasci-092724-050810","DOIUrl":"10.1146/annurev-biodatasci-092724-050810","url":null,"abstract":"<p><p>The vast potential of observational healthcare data in biomedical discovery remains largely unrealized because clinical records are fragmented, unstructured, and generated for patient care rather than research. Clinical knowledge representation (KR) helps to bridge this gap by encoding information in standardized, computable formats that preserve meaning and context. This review examines KR across the clinical data life cycle, from the generation of data in healthcare settings to their transformation for secondary use and eventual application in data science. We highlight foundational components such as standardized terminologies, ontologies, and common data models that enable data harmonization and interoperability. We further discuss how these structured representations support multimodal data integration and the development of more accurate, interpretable artificial intelligence models. Adopting a semantics-first approach to KR is essential for transforming fragmented clinical data into reusable, trustworthy knowledge that advances data-driven discovery and improves patient care.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"239-263"},"PeriodicalIF":8.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783334","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}
引用次数: 0
Precision Oncology: Multimodal and Multiscale Methods to Promote Mechanistic Understanding. 精确肿瘤学：多模式和多尺度方法促进机制理解。
IF 8.1
Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-03-06 DOI: 10.1146/annurev-biodatasci-092724-043507
Kivilcim Ozturk, Adam Klie, Hannah Carter
{"title":"Precision Oncology: Multimodal and Multiscale Methods to Promote Mechanistic Understanding.","authors":"Kivilcim Ozturk, Adam Klie, Hannah Carter","doi":"10.1146/annurev-biodatasci-092724-043507","DOIUrl":"10.1146/annurev-biodatasci-092724-043507","url":null,"abstract":"<p><p>Precision medicine aims to tailor treatment to the individual, improving medical outcomes and quality of life. Realizing this vision requires understanding how disease mechanisms and drug responses vary across patients. Advances in molecular profiling have enabled detailed measurement of genetic, epigenetic, spatial, and imaging features at multiple biological scales, from single cells to tissues. These rich and complementary data promise insight into the drivers of human disease where individual data layers have often provided an incomplete picture. Increasingly, studies span multiple measurement modalities and scales, presenting both opportunities and challenges. Central among these is how to combine data types to uncover actionable biology. This review surveys computational strategies for analyzing multimodal and multiscale datasets, distinguishing between approaches that treat each modality independently and those that perform true integrative modeling. We highlight emerging methods, with a focus on oncology, where these tools are helping to reveal mechanisms and guide therapeutic decisions.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"1-26"},"PeriodicalIF":8.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147370407","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}
引用次数: 0
Mechanistic Mutational Scanning to Uncover the Secret Life of Proteins. 机械突变扫描揭示蛋白质的秘密生命。
IF 8.1
Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-04-28 DOI: 10.1146/annurev-biodatasci-092624-104436
Matthew K Howard, Willow Coyote-Maestas
{"title":"Mechanistic Mutational Scanning to Uncover the Secret Life of Proteins.","authors":"Matthew K Howard, Willow Coyote-Maestas","doi":"10.1146/annurev-biodatasci-092624-104436","DOIUrl":"10.1146/annurev-biodatasci-092624-104436","url":null,"abstract":"<p><p>Deep mutational scanning (DMS) has emerged as a transformative tool for dissecting individual protein function and broader cell biology. DMS methods enable comprehensive interrogation of sequence-function relationships by systematically testing thousands of genetic variants within a pool for their effect(s) on diverse phenotypes. This review focuses on how these approaches are revolutionizing mechanistic protein biology, cell biology, and pharmacological research. We present a conceptual framework to organize genetic perturbations with phenotypic readouts along a nested cellular continuum-from protein folding and biogenesis to trafficking, posttranslational modification, protein-protein interactions, and downstream signaling. We highlight recent advances in mapping allosteric networks, pharmacologic mechanisms, and multiphenotype screening technologies. These mechanistic insights are reshaping our understanding of protein function at the residue level and informing our understanding of drug action, allostery, the interpretation of rare variants, and protein engineering strategies.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"265-283"},"PeriodicalIF":8.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783350","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}
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。
IF 6
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
{"title":"Mapping, Modeling, and Reprogramming Cell-Fate Decision-Making Systems.","authors":"Lucy Ham, Taylor E Woodward, Megan A Coomer, Michael P H Stumpf","doi":"10.1146/annurev-biodatasci-101424-121439","DOIUrl":"10.1146/annurev-biodatasci-101424-121439","url":null,"abstract":"<p><p>Many cellular processes involve information processing and decision-making. We can probe these processes at increasing molecular detail. The analysis of heterogeneous data remains a challenge that requires new ways of thinking about cells in quantitative, predictive, and mechanistic ways. We discuss the role of mathematical models in the context of cell-fate decision-making systems across the tree of life. Complex multicellular organisms have been a particular focus, but single-celled organisms also have to sense and respond to their environment. We center our discussion around the idea of design principles that we can learn from observations and modeling and exploit in order to (re)-design or guide cellular behavior.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"537-562"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984534","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}
引用次数: 0
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。
IF 6
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":"<p><p>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.</p>","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}
引用次数: 0
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。
IF 6
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":"<p><p>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.</p>","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}
引用次数: 0
Foundation Models for Translational Cancer Biology. 转化癌症生物学基础模型。
IF 6
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":"<p><p>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.</p>","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}
引用次数: 0
Clinical Text Generation: Are We There Yet? 临床文本生成：我们做到了吗？
IF 6
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
{"title":"Clinical Text Generation: Are We There Yet?","authors":"Nicolas Hiebel, Olivier Ferret, Karën Fort, Aurélie Névéol","doi":"10.1146/annurev-biodatasci-103123-095202","DOIUrl":"10.1146/annurev-biodatasci-103123-095202","url":null,"abstract":"<p><p>Generative artificial intelligence (AI), operationalized as large language models, is increasingly used in the biomedical field to assist with a range of text processing tasks including text classification, information extraction, and decision support. In this article, we focus on the primary purpose of generative language models, namely the production of unstructured text. We review past and current methods used to generate text as well as methods for evaluating open text generation, i.e., in contexts where no reference text is available for comparison. We discuss clinical applications that can benefit from high quality, ethically designed text generation, such as clinical note generation and synthetic text generation in support of secondary use of health data. We also raise awareness of the risks involved with generative AI such as overconfidence in outputs due to anthropomorphism and the risk of representational and allocation harms due to biases.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"173-198"},"PeriodicalIF":6.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658875","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}
引用次数: 0
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。
IF 8.1
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
{"title":"Strategies for Creating Robust Patient Groups to Study Diverse Conditions with Electronic Health Records.","authors":"Grace D Ramey, Hannah Takasuka, John A Capra","doi":"10.1146/annurev-biodatasci-020722-114525","DOIUrl":"10.1146/annurev-biodatasci-020722-114525","url":null,"abstract":"<p><p>The growth of electronic health record (EHR) databases in size and availability has created an unprecedented opportunity to better understand human health and disease. However, conducting robust EHR studies requires careful filtering criteria and study design, as EHRs pose several challenges that can confound analyses and lead to inaccurate results. Here we review these challenges and make suggestions about how to avoid or adjust for major confounders and biases in common EHR study designs. We further highlight qualities of EHR data that make different diseases more or less feasible for study. These recommendations for conducting research using EHRs will help inform database selection, improve reproducibility of results across the field, and enhance the validity of study results.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"317-340"},"PeriodicalIF":8.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书