Annual Review of Biomedical Data Science最新文献

Foundation Models for Translational Cancer Biology.

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Annual Review of Biomedical Data Science Pub 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":"https://doi.org/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":""},"PeriodicalIF":7.0,"publicationDate":"2025-01-29","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

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 : 2025-01-14 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":"https://doi.org/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":""},"PeriodicalIF":7.0,"publicationDate":"2025-01-14","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}

引用次数: 0

Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference. 空间转录组学为轨迹推断带来新的挑战和机遇

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Annual Review of Biomedical Data Science Pub Date : 2024-11-14 DOI: 10.1146/annurev-biodatasci-040324-030052

Matthieu Heitz, Yujia Ma, Sharvaj Kubal, Geoffrey Schiebinger

引用次数: 0

Centralized and Federated Models for the Analysis of Clinical Data. 临床数据分析的集中模式和联合模式。

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Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-122220-115746

Ruowang Li, Joseph D Romano, Yong Chen, Jason H Moore

引用次数: 0

The Evolutionary Interplay of Somatic and Germline Mutation Rates. 体细胞和种系突变率在进化过程中的相互作用

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Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102523-104225

Annabel C Beichman, Luke Zhu, Kelley Harris

引用次数: 0

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes. 绘制细胞和疾病表型的多尺度蛋白质组组织图。

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Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102423-113534

Anthony Cesnik, Leah V Schaffer, Ishan Gaur, Mayank Jain, Trey Ideker, Emma Lundberg

{"title":"Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.","authors":"Anthony Cesnik, Leah V Schaffer, Ishan Gaur, Mayank Jain, Trey Ideker, Emma Lundberg","doi":"10.1146/annurev-biodatasci-102423-113534","DOIUrl":"10.1146/annurev-biodatasci-102423-113534","url":null,"abstract":"While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"369-389"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946150","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

Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes. 在阿尔茨海默病研究中采用信息学策略：从遗传学、多组学、生物标记物到临床结果的回顾。

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Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102423-121021

Jingxuan Bao, Brian N Lee, Junhao Wen, Mansu Kim, Shizhuo Mu, Shu Yang, Christos Davatzikos, Qi Long, Marylyn D Ritchie, Li Shen

{"title":"Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes.","authors":"Jingxuan Bao, Brian N Lee, Junhao Wen, Mansu Kim, Shizhuo Mu, Shu Yang, Christos Davatzikos, Qi Long, Marylyn D Ritchie, Li Shen","doi":"10.1146/annurev-biodatasci-102423-121021","DOIUrl":"10.1146/annurev-biodatasci-102423-121021","url":null,"abstract":"Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"391-418"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11525791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141288709","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

Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives. 空间分辨单细胞图像学：方法、挑战和未来展望》。

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Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102523-103640

Felipe Segato Dezem, Wani Arjumand, Hannah DuBose, Natalia Silva Morosini, Jasmine Plummer

{"title":"Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives.","authors":"Felipe Segato Dezem, Wani Arjumand, Hannah DuBose, Natalia Silva Morosini, Jasmine Plummer","doi":"10.1146/annurev-biodatasci-102523-103640","DOIUrl":"10.1146/annurev-biodatasci-102523-103640","url":null,"abstract":"Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"131-153"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071246","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

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine. 在多模态 Omics 数据整合中利用人工智能：为精准医学的下一个前沿领域铺平道路。

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Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102523-103801

Yonghyun Nam, Jaesik Kim, Sang-Hyuk Jung, Jakob Woerner, Erica H Suh, Dong-Gi Lee, Manu Shivakumar, Matthew E Lee, Dokyoon Kim

{"title":"Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine.","authors":"Yonghyun Nam, Jaesik Kim, Sang-Hyuk Jung, Jakob Woerner, Erica H Suh, Dong-Gi Lee, Manu Shivakumar, Matthew E Lee, Dokyoon Kim","doi":"10.1146/annurev-biodatasci-102523-103801","DOIUrl":"10.1146/annurev-biodatasci-102523-103801","url":null,"abstract":"The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"225-250"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071239","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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Privacy-Enhancing Technologies in Biomedical Data Science. 生物医学数据科学中的隐私增强技术。

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Annual Review of Biomedical Data Science Pub Date : 2024-08-01 DOI: 10.1146/annurev-biodatasci-120423-120107

Hyunghoon Cho, David Froelicher, Natnatee Dokmai, Anupama Nandi, Shuvom Sadhuka, Matthew M Hong, Bonnie Berger

引用次数: 0