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Annual Review of Biomedical Data Science最新文献

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The Value Proposition of Coordinated Population Cohorts Across Africa. 全非洲协调人口群组的价值主张。
IF 7
Annual Review of Biomedical Data Science Pub Date : 2024-08-01 DOI: 10.1146/annurev-biodatasci-020722-015026
Michèle Ramsay, Amelia C Crampin, Ayaga A Bawah, Evelyn Gitau, Kobus Herbst
{"title":"The Value Proposition of Coordinated Population Cohorts Across Africa.","authors":"Michèle Ramsay, Amelia C Crampin, Ayaga A Bawah, Evelyn Gitau, Kobus Herbst","doi":"10.1146/annurev-biodatasci-020722-015026","DOIUrl":"10.1146/annurev-biodatasci-020722-015026","url":null,"abstract":"<p><p>Building longitudinal population cohorts in Africa for coordinated research and surveillance can influence the setting of national health priorities, lead to the introduction of appropriate interventions, and provide evidence for targeted treatment, leading to better health across the continent. However, compared to cohorts from the global north, longitudinal continental African population cohorts remain scarce, are relatively small in size, and lack data complexity. As infections and noncommunicable diseases disproportionately affect Africa's approximately 1.4 billion inhabitants, African cohorts present a unique opportunity for research and surveillance. High genetic diversity in African populations and multiomic research studies, together with detailed phenotyping and clinical profiling, will be a treasure trove for discovery. The outcomes, including novel drug targets, biological pathways for disease, and gene-environment interactions, will boost precision medicine approaches, not only in Africa but across the globe.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044149","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
Graph Artificial Intelligence in Medicine. 图谱人工智能在医学中的应用。
IF 7
Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-110723-024625
Ruth Johnson, Michelle M Li, Ayush Noori, Owen Queen, Marinka Zitnik
{"title":"Graph Artificial Intelligence in Medicine.","authors":"Ruth Johnson, Michelle M Li, Ayush Noori, Owen Queen, Marinka Zitnik","doi":"10.1146/annurev-biodatasci-110723-024625","DOIUrl":"10.1146/annurev-biodatasci-110723-024625","url":null,"abstract":"<p><p>In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way toward clinically meaningful predictions.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946148","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
Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity. 预测 T 细胞介导的适应性免疫中关键相互作用的计算方法。
IF 7
Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102423-122741
Ryan Ehrlich, Eric Glynn, Mona Singh, Dario Ghersi
{"title":"Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity.","authors":"Ryan Ehrlich, Eric Glynn, Mona Singh, Dario Ghersi","doi":"10.1146/annurev-biodatasci-102423-122741","DOIUrl":"10.1146/annurev-biodatasci-102423-122741","url":null,"abstract":"<p><p>The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946171","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
Generating Clinical-Grade Gene-Disease Validity Classifications Through the ClinGen Data Platforms. 通过 ClinGen 数据平台生成临床级基因-疾病有效性分类。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2024-04-25 DOI: 10.1146/annurev-biodatasci-102423-112456
M. W. Wright, C. Thaxton, T. Nelson, Marina T. DiStefano, J. Savatt, Matthew H Brush, Gloria Cheung, Mark E. Mandell, Bryan Wulf, T. J. Ward, Scott Goehringer, Terry O'Neill, Phil Weller, C. Preston, Ingrid M Keseler, Jennifer L Goldstein, Natasha T Strande, Jennifer L McGlaughon, Danielle R Azzariti, Ineke Cordova, Hannah Dziadzio, Lawrence Babb, Kevin Riehle, A. Milosavljevic, Christa Lese Martin, Heidi L. Rehm, S. Plon, Jonathan S. Berg, E. Riggs, Teri E Klein
{"title":"Generating Clinical-Grade Gene-Disease Validity Classifications Through the ClinGen Data Platforms.","authors":"M. W. Wright, C. Thaxton, T. Nelson, Marina T. DiStefano, J. Savatt, Matthew H Brush, Gloria Cheung, Mark E. Mandell, Bryan Wulf, T. J. Ward, Scott Goehringer, Terry O'Neill, Phil Weller, C. Preston, Ingrid M Keseler, Jennifer L Goldstein, Natasha T Strande, Jennifer L McGlaughon, Danielle R Azzariti, Ineke Cordova, Hannah Dziadzio, Lawrence Babb, Kevin Riehle, A. Milosavljevic, Christa Lese Martin, Heidi L. Rehm, S. Plon, Jonathan S. Berg, E. Riggs, Teri E Klein","doi":"10.1146/annurev-biodatasci-102423-112456","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102423-112456","url":null,"abstract":"Clinical genetic laboratories must have access to clinically validated biomedical data for precision medicine. A lack of accessibility, normalized structure, and consistency in evaluation complicates interpretation of disease causality, resulting in confusion in assessing the clinical validity of genes and genetic variants for diagnosis. A key goal of the Clinical Genome Resource (ClinGen) is to fill the knowledge gap concerning the strength of evidence supporting the role of a gene in a monogenic disease, which is achieved through a process known as Gene-Disease Validity curation. Here we review the work of ClinGen in developing a curation infrastructure that supports the standardization, harmonization, and dissemination of Gene-Disease Validity data through the creation of frameworks and the utilization of common data standards. This infrastructure is based on several applications, including the ClinGen GeneTracker, Gene Curation Interface, Data Exchange, GeneGraph, and website.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140656598","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
Bringing the Genomic Revolution to Comparative Oncology: Human and Dog Cancers. 将基因组革命引入比较肿瘤学：人类和狗的癌症。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2024-04-22 DOI: 10.1146/annurev-biodatasci-102423-111936
James A. Cahill, Leslie A. Smith, Soumya Gottipati, Tina Salehi Torabi, Kiley Graim
{"title":"Bringing the Genomic Revolution to Comparative Oncology: Human and Dog Cancers.","authors":"James A. Cahill, Leslie A. Smith, Soumya Gottipati, Tina Salehi Torabi, Kiley Graim","doi":"10.1146/annurev-biodatasci-102423-111936","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102423-111936","url":null,"abstract":"Dogs are humanity's oldest friend, the first species we domesticated 20,000-40,000 years ago. In this unequaled collaboration, dogs have inadvertently but serendipitously been molded into a potent human cancer model. Unlike many common model species, dogs are raised in the same environment as humans and present with spontaneous tumors with human-like comorbidities, immunocompetency, and heterogeneity. In breast, bladder, blood, and several pediatric cancers, in-depth profiling of dog and human tumors has established the benefits of the dog model. In addition to this clinical and molecular similarity, veterinary studies indicate that domestic dogs have relatively high tumor incidence rates. As a result, there are a plethora of data for analysis, the statistical power of which is bolstered by substantial breed-specific variability. As such, dog tumors provide a unique opportunity to interrogate the molecular factors underpinning cancer and facilitate the modeling of new therapeutic targets. This review discusses the emerging field of comparative oncology, how it complements human and rodent cancer studies, and where challenges remain, given the rapid proliferation of genomic resources. Increasingly, it appears that human's best friend is becoming an irreplaceable component of oncology research.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140676397","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
Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective. 人类遗传学和基因组学用于药物靶点识别和优先排序：开放靶点的视角。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2024-04-12 DOI: 10.1146/annurev-biodatasci-102523-103838
Ellen M. McDonagh, G. Trynka, Mark McCarthy, E. R. Holzinger, Shameer Khader, N. Nakić, Xinli Hu, Helena Cornu, Ian Dunham, David Hulcoop
{"title":"Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective.","authors":"Ellen M. McDonagh, G. Trynka, Mark McCarthy, E. R. Holzinger, Shameer Khader, N. Nakić, Xinli Hu, Helena Cornu, Ian Dunham, David Hulcoop","doi":"10.1146/annurev-biodatasci-102523-103838","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102523-103838","url":null,"abstract":"Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712311","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
AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles. AlphaFold 和蛋白质折叠：尚未死亡！构象组合是前沿。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2024-04-11 DOI: 10.1146/annurev-biodatasci-102423-011435
Gregory R Bowman
{"title":"AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles.","authors":"Gregory R Bowman","doi":"10.1146/annurev-biodatasci-102423-011435","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102423-011435","url":null,"abstract":"Like the black knight in the classic Monty Python movie, grand scientific challenges such as protein folding are hard to finish off. Notably, AlphaFold is revolutionizing structural biology by bringing highly accurate structure prediction to the masses and opening up innumerable new avenues of research. Despite this enormous success, calling structure prediction, much less protein folding and related problems, \"solved\" is dangerous, as doing so could stymie further progress. Imagine what the world would be like if we had declared flight solved after the first commercial airlines opened and stopped investing in further research and development. Likewise, there are still important limitations to structure prediction that we would benefit from addressing. Moreover, we are limited in our understanding of the enormous diversity of different structures a single protein can adopt (called a conformational ensemble) and the dynamics by which a protein explores this space. What is clear is that conformational ensembles are critical to protein function, and understanding this aspect of protein dynamics will advance our ability to design new proteins and drugs.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712812","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
Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth. 生物医学数据科学、人工智能与伦理：面对爆炸式增长的挑战。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2024-04-10 DOI: 10.1146/annurev-biodatasci-102623-104553
Carole A Federico, Artem A. Trotsyuk
{"title":"Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth.","authors":"Carole A Federico, Artem A. Trotsyuk","doi":"10.1146/annurev-biodatasci-102623-104553","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102623-104553","url":null,"abstract":"Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140718953","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
Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities. 药物再利用的计算方法：方法、挑战和机遇》。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2024-04-10 DOI: 10.1146/annurev-biodatasci-110123-025333
H. Cousins, Gowri Nayar, Russ B Altman
{"title":"Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities.","authors":"H. Cousins, Gowri Nayar, Russ B Altman","doi":"10.1146/annurev-biodatasci-110123-025333","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-110123-025333","url":null,"abstract":"Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717928","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
Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective. 应对生物医学数据不平等的挑战：人工智能视角。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-04-27 DOI: 10.1146/annurev-biodatasci-020722-020704
Yan Gao, Teena Sharma, Yan Cui
{"title":"Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective.","authors":"Yan Gao,&nbsp;Teena Sharma,&nbsp;Yan Cui","doi":"10.1146/annurev-biodatasci-020722-020704","DOIUrl":"10.1146/annurev-biodatasci-020722-020704","url":null,"abstract":"<p><p>Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential to precision medicine. However, the existing biomedical data, which are a vital resource and foundation for developing medical AI models, do not reflect the diversity of the human population. The low representation in biomedical data has become a significant health risk for non-European populations, and the growing application of AI opens a new pathway for this health risk to manifest and amplify. Here we review the current status of biomedical data inequality and present a conceptual framework for understanding its impacts on machine learning. We also discuss the recent advances in algorithmic interventions for mitigating health disparities arising from biomedical data inequality. Finally, we briefly discuss the newly identified disparity in data quality among ethnic groups and its potential impacts on machine learning.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10529864/pdf/nihms-1913459.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960491","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: 3
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