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

Data Science Methods for Real-World Evidence Generation in Real-World Data. 在真实世界数据中生成证据的数据科学方法。

IF 7

Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102423-113220

Fang Liu

{"title":"Data Science Methods for Real-World Evidence Generation in Real-World Data.","authors":"Fang Liu","doi":"10.1146/annurev-biodatasci-102423-113220","DOIUrl":"10.1146/annurev-biodatasci-102423-113220","url":null,"abstract":"In the healthcare landscape, data science (DS) methods have emerged as indispensable tools to harness real-world data (RWD) from various data sources such as electronic health records, claim and registry data, and data gathered from digital health technologies. Real-world evidence (RWE) generated from RWD empowers researchers, clinicians, and policymakers with a more comprehensive understanding of real-world patient outcomes. Nevertheless, persistent challenges in RWD (e.g., messiness, voluminousness, heterogeneity, multimodality) and a growing awareness of the need for trustworthy and reliable RWE demand innovative, robust, and valid DS methods for analyzing RWD. In this article, I review some common current DS methods for extracting RWE and valuable insights from complex and diverse RWD. This article encompasses the entire RWE-generation pipeline, from study design with RWD to data preprocessing, exploratory analysis, methods for analyzing RWD, and trustworthiness and reliability guarantees, along with data ethics considerations and open-source tools. This review, tailored for an audience that may not be experts in DS, aspires to offer a systematic review of DS methods and assists readers in selecting suitable DS methods and enhancing the process of RWE generation for addressing their specific challenges.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"201-224"},"PeriodicalIF":7.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946147","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 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

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":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"345-368"},"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":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":"295-316"},"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

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

引用次数: 3

Virus-Derived Small RNAs and microRNAs in Health and Disease. 病毒衍生小rna和微rna在健康和疾病中的作用。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-122220-111429

Vasileios Gouzouasis, Spyros Tastsoglou, Antonis Giannakakis, Artemis G Hatzigeorgiou

引用次数: 0

Computational Methods for Single-Cell Proteomics. 单细胞蛋白质组学的计算方法。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-04-11 DOI: 10.1146/annurev-biodatasci-020422-050255

Sophia M Guldberg, Trine Line Hauge Okholm, Elizabeth E McCarthy, Matthew H Spitzer

引用次数: 0

Gene Interactions in Human Disease Studies-Evidence Is Mounting. 人类疾病研究中的基因相互作用——证据越来越多。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-102022-120818

Pankhuri Singhal, Shefali Setia Verma, Marylyn D Ritchie

{"title":"Gene Interactions in Human Disease Studies-Evidence Is Mounting.","authors":"Pankhuri Singhal, Shefali Setia Verma, Marylyn D Ritchie","doi":"10.1146/annurev-biodatasci-102022-120818","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102022-120818","url":null,"abstract":"Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"6 ","pages":"377-395"},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960535","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: 3

Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants. 多样性在精准医学中的重要性:跨祖先群体遗传关联的普遍性，有助于更好地识别疾病易感性变异。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-05-17 DOI: 10.1146/annurev-biodatasci-122220-113250

Lauren A Cruz, Jessica N Cooke Bailey, Dana C Crawford

{"title":"Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants.","authors":"Lauren A Cruz, Jessica N Cooke Bailey, Dana C Crawford","doi":"10.1146/annurev-biodatasci-122220-113250","DOIUrl":"10.1146/annurev-biodatasci-122220-113250","url":null,"abstract":"Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"6 ","pages":"339-356"},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10720270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960536","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

Human Microbiomes and Disease for the Biomedical Data Scientist. 生物医学数据科学家的人类微生物组和疾病。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-020722-043017

Jonathan L Golob

引用次数: 0