Annual Review of Biomedical Data Science最新文献_第2页

Informatics Infrastructure for the Learning Health System. 学习型健康系统的信息基础设施。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-04-28 DOI: 10.1146/annurev-biodatasci-092624-103225

Jessica D Tenenbaum, James J Cimino, David A Dorr, Peter J Embí, Genevieve Melton-Meaux, Marylyn D Ritchie, Mark G Weiner, Charles P Friedman

引用次数: 0

Statistical Methods for Understanding Trajectories in Genetic Epidemiology. 理解遗传流行病学轨迹的统计方法。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-03-25 DOI: 10.1146/annurev-biodatasci-092724-035434

Geng Wang, Lavinia Paternoster, Nicole M Warrington

引用次数: 0

Sex and the Genome: Divergent Genetic Architecture Across the Human Lifespan. 性与基因组：人类一生中不同的基因结构。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-05-19 DOI: 10.1146/annurev-biodatasci-092724-060518

Jagyashila Das, Pauline Gachanja, Aisha Sallah, Ankita Jain, Yogasudha Veturi, Rachel L Kember, Shefali Setia Verma

{"title":"Sex and the Genome: Divergent Genetic Architecture Across the Human Lifespan.","authors":"Jagyashila Das, Pauline Gachanja, Aisha Sallah, Ankita Jain, Yogasudha Veturi, Rachel L Kember, Shefali Setia Verma","doi":"10.1146/annurev-biodatasci-092724-060518","DOIUrl":"10.1146/annurev-biodatasci-092724-060518","url":null,"abstract":"Males and females share nearly identical genomes yet exhibit profound differences in disease susceptibility, progression, and treatment response across the lifespan. These sex differences arise from evolutionary conflicts in which genetic variants confer opposing fitness effects between sexes, maintained by balancing selection. This review examines how sex shapes complex trait architecture from early childhood through postreproductive life. Early childhood reveals intrinsic genetic sexual dimorphism before hormonal activation. During reproductive years, sex-specific metabolic programming manifests through divergent adipose distribution and cardiometabolic risk, while autoimmune diseases demonstrate a fourfold female bias driven by incomplete X-inactivation. Postreproductive phases reveal antagonistic pleiotropy, in which variants that are beneficial for early reproduction increase late-life disease vulnerability. Systematic exclusion of X chromosome data from ∼75% of genome-wide association studies has obscured critical therapeutic targets. Sex-stratified genomic analyses consistently uncover effect sizes and risk loci that are invisible in sex-combined models. We argue that precision medicine must incorporate sex as a fundamental axis of genomic stratification across development, rather than treating it as a covariate to be adjusted away.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"423-448"},"PeriodicalIF":8.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147975951","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

Building Toward a Future for Electronic Health Record Systems. 构建电子健康记录系统的未来。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-05-06 DOI: 10.1146/annurev-biodatasci-092724-050949

S Trent Rosenbloom, A Jay Holmgren, Bryan D Steitz

{"title":"Building Toward a Future for Electronic Health Record Systems.","authors":"S Trent Rosenbloom, A Jay Holmgren, Bryan D Steitz","doi":"10.1146/annurev-biodatasci-092724-050949","DOIUrl":"10.1146/annurev-biodatasci-092724-050949","url":null,"abstract":"Health information technology tools, including electronic health records, are ubiquitous in healthcare across the United States. Despite the promise and opportunity of these tools, their benefits have been uneven while also having the unintended consequence of imposing substantial administrative and documentation challenges that are often linked to clinician burnout. The adoption of these tools has been shaped by financial incentives, regulatory programs, and sociotechnical demands tied to reimbursement and quality measurement. Patient-facing technologies, including patient portals and remote monitoring, expand access and engagement, but there are disparities in patient use and they often create burden for clinicians. Advances in artificial intelligence, and particularly large language models, now enable automated documentation, ambient capture of clinical encounters, and clinical decision support-offering potential to reduce clinician burden and enhance care delivery. We envision a future of health information technology in which these tools are fully embedded and integrated into the clinical environment such that they streamline clinical work and workflow, optimize decision-making, and improve patient engagement.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"311-336"},"PeriodicalIF":8.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843588","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

Methods for Mapping and Analyzing Context-Specific Protein-Protein Interaction Networks. 定位和分析上下文特异性蛋白质-蛋白质相互作用网络的方法。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-04-21 DOI: 10.1146/annurev-biodatasci-092724-033748

Tavis J Reed

{"title":"Methods for Mapping and Analyzing Context-Specific Protein-Protein Interaction Networks.","authors":"Tavis J Reed","doi":"10.1146/annurev-biodatasci-092724-033748","DOIUrl":"10.1146/annurev-biodatasci-092724-033748","url":null,"abstract":"Protein-protein interactions (PPIs) form highly dynamic and context-specific networks that vary across cell types and perturbation states. Mapping and understanding these context-dependent interactomes are essential for unraveling cellular organization and function. This review examines computational and experimental approaches for mapping and analyzing context-specific PPI networks. Computational methods refine global interactomes using context-specific expression data, literature curation, or machine learning-based integration of omics datasets, thereby constructing interactomes that consist of both physical and functional associations. Experimental approaches, including crosslinking mass spectrometry, cofractionation mass spectrometry, and denaturation-based mass spectrometry methods, directly capture physical PPIs within defined biological contexts. This review further discusses analytical frameworks for extracting biological insights from these networks, including protein complex detection, network embedding, differential analysis, functional module detection, and biomarker discovery. Finally, future directions are highlighted involving multiomic integration, enhanced spatial and temporal resolution, and artificial intelligence-driven network interpretation for further enriching the biological insights that can be obtained from context-specific PPI networks.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"171-196"},"PeriodicalIF":8.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783346","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 Data Science of Health in Space. 空间健康数据科学。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-05-18 DOI: 10.1146/annurev-biodatasci-092724-045605

Julian Daniel Sunday Willett, JangKeun Kim, Anurag Sakharkar, Jiwoon Park, Akua A Agyemang, Eyup Alper Saygili, Jacqueline Proszynski, Theodore M Nelson, Christopher E Mason

{"title":"The Data Science of Health in Space.","authors":"Julian Daniel Sunday Willett, JangKeun Kim, Anurag Sakharkar, Jiwoon Park, Akua A Agyemang, Eyup Alper Saygili, Jacqueline Proszynski, Theodore M Nelson, Christopher E Mason","doi":"10.1146/annurev-biodatasci-092724-045605","DOIUrl":"10.1146/annurev-biodatasci-092724-045605","url":null,"abstract":"Spaceflight exposes humans to unique stressors that remodel biology. Space health is emerging as a data science discipline grounded in multiomic atlases, harmonized biobanks, and open, findable, accessible, interoperable, and reusable (FAIR)-aligned infrastructures. We review this ecosystem, including the National Aeronautics and Space Administration (NASA)'s GeneLab; the Open Science Data Repository, European Space Agency, and Japan Aerospace Exploration Agency (JAXA) platforms; the Space Omics and Medical Atlas; and mission-specific repositories, and how it is used to integrate heterogeneous omics with clinical, environmental, and digital phenotypes. We highlight machine learning and causal inference approaches to identify conserved signatures of risk and resilience and to align astronaut datasets with large terrestrial cohorts and disease models. Spaceflight functions as an accelerated model of aging and systems-level stress, enabling discovery of biomarkers and countermeasures with reciprocal benefits for Earth-based medicine. Finally, we outline future directions, including countermeasure-prioritization pipelines; astronaut digital twins and virtual organs; operational analytics and wearables for personalized risk management; and federated, privacy-preserving data ecosystems that extend predictive, individualized space health to the operational edge.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"359-378"},"PeriodicalIF":8.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147975908","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

Comparative Oncology: Evolutionary Perspectives, Emerging Discoveries, and Analytical Advances. 比较肿瘤学：进化观点、新发现和分析进展。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-04-14 DOI: 10.1146/annurev-biodatasci-092724-030138

Vahid Nikoonejad Fard, Marc Tollis

{"title":"Comparative Oncology: Evolutionary Perspectives, Emerging Discoveries, and Analytical Advances.","authors":"Vahid Nikoonejad Fard, Marc Tollis","doi":"10.1146/annurev-biodatasci-092724-030138","DOIUrl":"10.1146/annurev-biodatasci-092724-030138","url":null,"abstract":"Cancer is an evolutionary inevitability of multicellularity that emerges when cooperative cellular networks collapse into competition for survival and replication. Despite decades of extensive research, progress in cancer treatment remains limited, partly because of the lack of an interdisciplinary perspective and an overreliance on murine models that fail to capture the full evolutionary spectrum and dynamics of oncogenesis. Comparative oncology is an interdisciplinary field that offers an eco-evolutionary framework to address these challenges by examining variation in cancer prevalence and defense mechanisms across the tree of life. In this review, we first explain why adopting an evolutionary lens is essential for understanding the challenge of cancer. Then, we review recent discoveries on genomic and life history determinants of cancer prevalence, remarkable species-specific cancer defense mechanisms, and methodological advances in analyzing cross-species cancer data. Insights from these findings can ultimately inform human cancer research and guide future directions in biodiversity conservation.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"117-140"},"PeriodicalIF":8.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692594","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

Toward Quantitative Domain-Motif Interactomes: State of the Art, Challenges, and Perspectives. 走向定量领域-基序相互作用组：现状、挑战和观点。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-05-18 DOI: 10.1146/annurev-biodatasci-092624-110614

Jesús Alvarado Valverde, Katja Luck

引用次数: 0

Large Language Models in Mental Health Research and Treatment. 心理健康研究和治疗中的大型语言模型。

IF 8.1

Annual Review of Biomedical Data Science Pub Date : 2026-08-01 Epub Date: 2026-05-18 DOI: 10.1146/annurev-biodatasci-092524-113527

Steven Mesquiti, Erik C Nook

引用次数: 0

Fold-Switching Proteins Push the Boundaries of Conformational Ensemble Prediction. 折叠开关蛋白推动了构象集合预测的边界。