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

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

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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

Single-Cell Multiomics. 单细胞多组学

IF 6

Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-05-09 DOI: 10.1146/annurev-biodatasci-020422-050645

Emily Flynn, Ana Almonte-Loya, Gabriela K Fragiadakis

{"title":"Single-Cell Multiomics.","authors":"Emily Flynn, Ana Almonte-Loya, Gabriela K Fragiadakis","doi":"10.1146/annurev-biodatasci-020422-050645","DOIUrl":"10.1146/annurev-biodatasci-020422-050645","url":null,"abstract":"Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"6 ","pages":"313-337"},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11146013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960510","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

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

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

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":"6 ","pages":"357-376"},"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。

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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":"6 ","pages":"443-464"},"PeriodicalIF":7.0,"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

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

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Annual Review of Biomedical Data Science Pub Date : 2023-03-07 DOI: 10.48550/arXiv.2303.03577

Peter Washington, D. Wall

{"title":"A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism","authors":"Peter Washington, D. Wall","doi":"10.48550/arXiv.2303.03577","DOIUrl":"https://doi.org/10.48550/arXiv.2303.03577","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. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"1 1","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47897781","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: 12

Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores. 开发更具通用性的多基因风险评分的挑战与机遇。

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Annual Review of Biomedical Data Science Pub Date : 2022-08-10 Epub Date: 2022-05-16 DOI: 10.1146/annurev-biodatasci-111721-074830

Ying Wang, Kristin Tsuo, Masahiro Kanai, Benjamin M Neale, Alicia R Martin

{"title":"Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores.","authors":"Ying Wang, Kristin Tsuo, Masahiro Kanai, Benjamin M Neale, Alicia R Martin","doi":"10.1146/annurev-biodatasci-111721-074830","DOIUrl":"10.1146/annurev-biodatasci-111721-074830","url":null,"abstract":"Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"5 ","pages":"293-320"},"PeriodicalIF":7.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9828290/pdf/nihms-1857872.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10555201","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