Annual Review of Biomedical Data Science最新文献_第4页

Challenges and Progress in Designing Broad-Spectrum Vaccines Against Rapidly Mutating Viruses. 针对快速变异病毒设计广谱疫苗的挑战与进展。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-020722-041304

Rishi Bedi, Nicholas L Bayless, Jacob Glanville

{"title":"Challenges and Progress in Designing Broad-Spectrum Vaccines Against Rapidly Mutating Viruses.","authors":"Rishi Bedi, Nicholas L Bayless, Jacob Glanville","doi":"10.1146/annurev-biodatasci-020722-041304","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-020722-041304","url":null,"abstract":"Viruses evolve to evade prior immunity, causing significant disease burden. Vaccine effectiveness deteriorates as pathogens mutate, requiring redesign. This is a problem that has grown worse due to population increase, global travel, and farming practices. Thus, there is significant interest in developing broad-spectrum vaccines that mitigate disease severity and ideally inhibit disease transmission without requiring frequent updates. Even in cases where vaccines against rapidly mutating pathogens have been somewhat effective, such as seasonal influenza and SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), designing vaccines that provide broad-spectrum immunity against routinely observed viral variation remains a desirable but not yet achieved goal. This review highlights the key theoretical advances in understanding the interplay between polymorphism and vaccine efficacy, challenges in designing broad-spectrum vaccines, and technology advances and possible avenues forward. We also discuss data-driven approaches for monitoring vaccine efficacy and predicting viral escape from vaccine-induced protection. In each case, we consider illustrative examples in vaccine development from influenza, SARS-CoV-2, and HIV (human immunodeficiency virus)-three examples of highly prevalent rapidly mutating viruses with distinct phylogenetics and unique histories of vaccine technology development.","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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960533","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 Identification of Functional Sequences and Variants in Noncoding DNA. 非编码DNA功能序列和变异的鉴定。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-122120-110102

Remo Monti, Uwe Ohler

引用次数: 0

A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. 自闭症神经精神表型的数据科学和机器学习综述和路线图。

IF 7

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-05-03 DOI: 10.1146/annurev-biodatasci-020722-125454

Peter Washington, Dennis P Wall

{"title":"A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.","authors":"Peter Washington, Dennis P Wall","doi":"10.1146/annurev-biodatasci-020722-125454","DOIUrl":"10.1146/annurev-biodatasci-020722-125454","url":null,"abstract":"Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11093217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960498","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

Strategies for the Genomic Analysis of Admixed Populations. 混血人群基因组分析策略。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-04-26 DOI: 10.1146/annurev-biodatasci-020722-014310

Taotao Tan, Elizabeth G Atkinson

引用次数: 0

Statistical Learning Methods for Neuroimaging Data Analysis with Applications. 神经影像数据分析的统计学习方法及其应用。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-020722-100353

Hongtu Zhu, Tengfei Li, Bingxin Zhao

引用次数: 6

Decoding Aging Hallmarks at the Single-Cell Level. 在单细胞水平上解码衰老特征。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-020722-120642

Shuai Ma, Xu Chi, Yusheng Cai, Zhejun Ji, Si Wang, Jie Ren, Guang-Hui Liu

引用次数: 8

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches. 超大型和基于结构的虚拟筛选方法的最新进展。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-020222-025013

Christoph Gorgulla

引用次数: 1

Noninvasive Prenatal Testing Using Circulating DNA and RNA: Advances, Challenges, and Possibilities. 使用循环DNA和RNA进行无创产前检测：进展、挑战和可能性。

IF 7

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-05-17 DOI: 10.1146/annurev-biodatasci-020722-094144

Mira N Moufarrej, Diana W Bianchi, Gary M Shaw, David K Stevenson, Stephen R Quake

引用次数: 0

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics. 转录组学和蛋白质组学中剪接变异体和异构体的鉴定。

IF 7

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-020722-044021

Taojunfeng Su, Michael A R Hollas, Ryan T Fellers, Neil L Kelleher

{"title":"Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics.","authors":"Taojunfeng Su, Michael A R Hollas, Ryan T Fellers, Neil L Kelleher","doi":"10.1146/annurev-biodatasci-020722-044021","DOIUrl":"10.1146/annurev-biodatasci-020722-044021","url":null,"abstract":"Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10840079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10339608","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

The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research. 我们所有人的数据和研究中心：为生物医学研究创建一个安全、可扩展和可持续的生态系统。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-122120-104825

Kelsey R Mayo, Melissa A Basford, Robert J Carroll, Moira Dillon, Heather Fullen, Jesse Leung, Hiral Master, Shimon Rura, Lina Sulieman, Nan Kennedy, Eric Banks, David Bernick, Asmita Gauchan, Lee Lichtenstein, Brandy M Mapes, Kayla Marginean, Steve L Nyemba, Andrea Ramirez, Charissa Rotundo, Keri Wolfe, Weiyi Xia, Romuladus E Azuine, Robert M Cronin, Joshua C Denny, Abel Kho, Christopher Lunt, Bradley Malin, Karthik Natarajan, Consuelo H Wilkins, Hua Xu, George Hripcsak, Dan M Roden, Anthony A Philippakis, David Glazer, Paul A Harris

{"title":"The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research.","authors":"Kelsey R Mayo, Melissa A Basford, Robert J Carroll, Moira Dillon, Heather Fullen, Jesse Leung, Hiral Master, Shimon Rura, Lina Sulieman, Nan Kennedy, Eric Banks, David Bernick, Asmita Gauchan, Lee Lichtenstein, Brandy M Mapes, Kayla Marginean, Steve L Nyemba, Andrea Ramirez, Charissa Rotundo, Keri Wolfe, Weiyi Xia, Romuladus E Azuine, Robert M Cronin, Joshua C Denny, Abel Kho, Christopher Lunt, Bradley Malin, Karthik Natarajan, Consuelo H Wilkins, Hua Xu, George Hripcsak, Dan M Roden, Anthony A Philippakis, David Glazer, Paul A Harris","doi":"10.1146/annurev-biodatasci-122120-104825","DOIUrl":"10.1146/annurev-biodatasci-122120-104825","url":null,"abstract":"The All of Us Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.","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/PMC11157478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10040579","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