Spatial transcriptomics (ST) brings new dimensions to the analysis of single-cell data. While some methods for data analysis can be ported over without major modifications, they are the exception rather than the rule. Trajectory inference (TI) methods in particular can suffer from significant challenges due to spatial batch effects in ST data. These can add independent sources of noise to each time point. Pioneering methods for TI on ST data have focused primarily on addressing the batch effects in physical arrangement, i.e., where tissues are deformed in different ways at different time points. However, other challenges arise due to the measurement granularity of ST technologies, as well as a bias from slicing. In this review, we examine the sources of these challenges, and we explore how they are addressed with current state-of-the-art STTI methods. We conclude by highlighting some opportunities for future method development.
空间转录组学(ST)为单细胞数据分析带来了新的维度。虽然有些数据分析方法无需进行重大修改即可移植,但它们只是例外,而不是常规。特别是轨迹推断(TI)方法,由于 ST 数据的空间批次效应,可能会面临巨大的挑战。这可能会给每个时间点增加独立的噪声源。ST 数据轨迹推断的开创性方法主要侧重于解决物理排列中的批次效应,即组织在不同时间点以不同方式变形。然而,由于 ST 技术的测量粒度以及切片产生的偏差,也带来了其他挑战。在本综述中,我们研究了这些挑战的来源,并探讨了当前最先进的 STTI 方法如何应对这些挑战。最后,我们强调了未来方法发展的一些机遇。
{"title":"Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference.","authors":"Matthieu Heitz, Yujia Ma, Sharvaj Kubal, Geoffrey Schiebinger","doi":"10.1146/annurev-biodatasci-040324-030052","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-040324-030052","url":null,"abstract":"<p><p>Spatial transcriptomics (ST) brings new dimensions to the analysis of single-cell data. While some methods for data analysis can be ported over without major modifications, they are the exception rather than the rule. Trajectory inference (TI) methods in particular can suffer from significant challenges due to spatial batch effects in ST data. These can add independent sources of noise to each time point. Pioneering methods for TI on ST data have focused primarily on addressing the batch effects in physical arrangement, i.e., where tissues are deformed in different ways at different time points. However, other challenges arise due to the measurement granularity of ST technologies, as well as a bias from slicing. In this review, we examine the sources of these challenges, and we explore how they are addressed with current state-of-the-art STTI methods. We conclude by highlighting some opportunities for future method development.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628467","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}
Pub Date : 2024-08-01Epub Date: 2024-07-24DOI: 10.1146/annurev-biodatasci-122220-115746
Ruowang Li, Joseph D Romano, Yong Chen, Jason H Moore
The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a comprehensive understanding of diseases. In this review, we describe two important approaches for the analysis of diverse clinical datasets, namely the centralized model and federated model. We compare and contrast the strengths and weaknesses inherent in each model and present recent progress in methodologies and their associated challenges. Finally, we present an outlook on the opportunities that both models hold for the future analysis of clinical data.
{"title":"Centralized and Federated Models for the Analysis of Clinical Data.","authors":"Ruowang Li, Joseph D Romano, Yong Chen, Jason H Moore","doi":"10.1146/annurev-biodatasci-122220-115746","DOIUrl":"10.1146/annurev-biodatasci-122220-115746","url":null,"abstract":"<p><p>The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a comprehensive understanding of diseases. In this review, we describe two important approaches for the analysis of diverse clinical datasets, namely the centralized model and federated model. We compare and contrast the strengths and weaknesses inherent in each model and present recent progress in methodologies and their associated challenges. Finally, we present an outlook on the opportunities that both models hold for the future analysis of clinical data.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"179-199"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899793","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}
Pub Date : 2024-08-01Epub Date: 2024-07-24DOI: 10.1146/annurev-biodatasci-102523-104225
Annabel C Beichman, Luke Zhu, Kelley Harris
Novel sequencing technologies are making it increasingly possible to measure the mutation rates of somatic cell lineages. Accurate germline mutation rate measurement technologies have also been available for a decade, making it possible to assess how this fundamental evolutionary parameter varies across the tree of life. Here, we review some classical theories about germline and somatic mutation rate evolution that were formulated using principles of population genetics and the biology of aging and cancer. We find that somatic mutation rate measurements, while still limited in phylogenetic diversity, seem consistent with the theory that selection to preserve the soma is proportional to life span. However, germline and somatic theories make conflicting predictions regarding which species should have the most accurate DNA repair. Resolving this conflict will require carefully measuring how mutation rates scale with time and cell division and achieving a better understanding of mutation rate pleiotropy among cell types.
新的测序技术使测量体细胞系突变率变得越来越可能。精确的种系突变率测量技术也已问世十年,这使得评估这一基本进化参数在整个生命树中的变化情况成为可能。在此,我们回顾了一些关于种系和体细胞突变率进化的经典理论,这些理论是利用群体遗传学和衰老与癌症生物学原理提出的。我们发现,体细胞突变率的测量结果虽然在系统发育多样性方面仍然有限,但似乎与保护体细胞的选择与寿命成正比的理论相一致。然而,生殖细胞理论和体细胞理论在预测哪个物种的 DNA 修复最准确方面存在冲突。要解决这一矛盾,需要仔细测量突变率如何随时间和细胞分裂而变化,并更好地了解细胞类型之间的突变率褶积性。
{"title":"The Evolutionary Interplay of Somatic and Germline Mutation Rates.","authors":"Annabel C Beichman, Luke Zhu, Kelley Harris","doi":"10.1146/annurev-biodatasci-102523-104225","DOIUrl":"10.1146/annurev-biodatasci-102523-104225","url":null,"abstract":"<p><p>Novel sequencing technologies are making it increasingly possible to measure the mutation rates of somatic cell lineages. Accurate germline mutation rate measurement technologies have also been available for a decade, making it possible to assess how this fundamental evolutionary parameter varies across the tree of life. Here, we review some classical theories about germline and somatic mutation rate evolution that were formulated using principles of population genetics and the biology of aging and cancer. We find that somatic mutation rate measurements, while still limited in phylogenetic diversity, seem consistent with the theory that selection to preserve the soma is proportional to life span. However, germline and somatic theories make conflicting predictions regarding which species should have the most accurate DNA repair. Resolving this conflict will require carefully measuring how mutation rates scale with time and cell division and achieving a better understanding of mutation rate pleiotropy among cell types.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"83-105"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140872288","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}
Pub Date : 2024-08-01Epub Date: 2024-07-24DOI: 10.1146/annurev-biodatasci-102423-113534
Anthony Cesnik, Leah V Schaffer, Ishan Gaur, Mayank Jain, Trey Ideker, Emma Lundberg
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.
{"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":"<p><p>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.</p>","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}
Pub Date : 2024-08-01Epub Date: 2024-07-24DOI: 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
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.
阿尔茨海默氏症(AD)是一个全国性的重大问题,影响到 580 万人,每年造成的损失超过 2,500 亿美元。然而,目前尚无治疗方法。因此,迫切需要有效的策略来发现阿兹海默症生物标志物,以用于疾病的早期检测和药物开发。在这篇综述中,我们从生物医学数据科学家的角度研究了AD,讨论了AD研究的四个基本组成部分:遗传学(G)、分子多组学(M)、多模态成像生物标志物(B)和临床结果(O)(统称为GMBO框架)。我们全面回顾了 GMBO 框架中每个组成部分的常用统计和信息学方法,并附有具有里程碑意义的 AD 研究的主要发现。我们的综述强调了多模态生物库数据在应对 AD 关键挑战(如早期诊断、疾病异质性和治疗开发)方面的潜力。我们指出了 AD 研究中的主要障碍,包括数据稀缺性和复杂性,并倡导加强合作、统一数据和采用先进的建模技术。这篇综述旨在成为了解当前 AD 研究中生物医学数据科学策略的重要指南,强调我们需要综合、多学科的方法来促进我们对 AD 的理解和管理。
{"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":"<p><p>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.</p>","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}
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.
{"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":"<p><p>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: (<i>a</i>) next-generation sequencing-based assays, (<i>b</i>) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (<i>c</i>) 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.</p>","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}
Pub Date : 2024-08-01Epub Date: 2024-07-24DOI: 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
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.
{"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":"<p><p>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.</p>","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}
Pub Date : 2024-08-01DOI: 10.1146/annurev-biodatasci-120423-120107
Hyunghoon Cho, David Froelicher, Natnatee Dokmai, Anupama Nandi, Shuvom Sadhuka, Matthew M Hong, Bonnie Berger
The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.
生物医学数据储存库的规模和种类迅速增加,引起了人们对隐私问题的关注。收集和共享人体数据的传统框架对隐私的保护有限,往往需要建立数据孤岛。隐私增强技术(PET)有望在保护隐私的同时,通过提供共享和分析敏感数据的方法来保护这些数据并扩大其使用范围。在此,我们回顾了著名的 PET,并说明了它们在推动生物医学发展方面的作用。我们描述了 PET 的关键用例及其最新技术进展,并重点介绍了 PET 在一系列生物医学领域的最新应用。最后,我们讨论了在生物医学数据科学中更广泛地采用 PETs 所面临的挑战和需要解决的社会问题。
{"title":"Privacy-Enhancing Technologies in Biomedical Data Science.","authors":"Hyunghoon Cho, David Froelicher, Natnatee Dokmai, Anupama Nandi, Shuvom Sadhuka, Matthew M Hong, Bonnie Berger","doi":"10.1146/annurev-biodatasci-120423-120107","DOIUrl":"10.1146/annurev-biodatasci-120423-120107","url":null,"abstract":"<p><p>The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"7 1","pages":"317-343"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11346580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044148","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}
Pub Date : 2024-08-01Epub Date: 2024-07-24DOI: 10.1146/annurev-biodatasci-102523-103821
Jarrod Shilts, Gavin J Wright
Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.
{"title":"Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication.","authors":"Jarrod Shilts, Gavin J Wright","doi":"10.1146/annurev-biodatasci-102523-103821","DOIUrl":"10.1146/annurev-biodatasci-102523-103821","url":null,"abstract":"<p><p>Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"155-177"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899795","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}
Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.
疾病轨迹被定义为连续的、方向性的疾病关联,在全人口电子医疗数据的可用性和充足的计算能力的推动下,疾病轨迹已成为一个热门研究领域。在此,我们以欧洲的研究为重点,概述了疾病轨迹研究,包括用于构建疾病轨迹的本体论和计算方法。我们还讨论了疾病轨迹的不同应用,从描述性风险识别到疾病进展、患者分层以及使用机器学习进行个性化预测。我们描述了该领域的挑战和机遇,这些挑战和机遇最终将受益于欧洲健康数据空间(European Health Data Space)等倡议,随着时间的推移,这些倡议将使分析来自数亿患者队列的数据成为可能。
{"title":"Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives.","authors":"Isabella Friis Jørgensen, Amalie Dahl Haue, Davide Placido, Jessica Xin Hjaltelin, Søren Brunak","doi":"10.1146/annurev-biodatasci-110123-041001","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-110123-041001","url":null,"abstract":"<p><p>Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"7 1","pages":"251-276"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044147","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}