Annual Review of Biomedical Data Science最新文献_第5页

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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":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/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

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":null,"pages":null},"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":null,"pages":null},"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

Genome Privacy and Trust. 基因组隐私与信任。

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Annual Review of Biomedical Data Science Pub Date : 2022-08-10 DOI: 10.1146/annurev-biodatasci-122120-021311

Gamze Gürsoy

引用次数: 1

Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine. 在大型人类基因研究中纳入非欧洲人群对加强精准医疗的重要性。

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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-122220-112550

Dan Ju, Daniel Hui, Dorothy A Hammond, Ambroise Wonkam, Sarah A Tishkoff

{"title":"Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine.","authors":"Dan Ju, Daniel Hui, Dorothy A Hammond, Ambroise Wonkam, Sarah A Tishkoff","doi":"10.1146/annurev-biodatasci-122220-112550","DOIUrl":"10.1146/annurev-biodatasci-122220-112550","url":null,"abstract":"One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9904154/pdf/nihms-1864817.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9545868","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

Cotranslational Mechanisms of Protein Biogenesis and Complex Assembly in Eukaryotes. 真核生物蛋白质生物生成和复合体组装的共翻译机制。

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Annual Review of Biomedical Data Science Pub Date : 2022-08-10 Epub Date: 2022-04-26 DOI: 10.1146/annurev-biodatasci-121721-095858

Fabián Morales-Polanco, Jae Ho Lee, Natália M Barbosa, Judith Frydman

{"title":"Cotranslational Mechanisms of Protein Biogenesis and Complex Assembly in Eukaryotes.","authors":"Fabián Morales-Polanco, Jae Ho Lee, Natália M Barbosa, Judith Frydman","doi":"10.1146/annurev-biodatasci-121721-095858","DOIUrl":"10.1146/annurev-biodatasci-121721-095858","url":null,"abstract":"The formation of protein complexes is crucial to most biological functions. The cellular mechanisms governing protein complex biogenesis are not yet well understood, but some principles of cotranslational and posttranslational assembly are beginning to emerge. In bacteria, this process is favored by operons encoding subunits of protein complexes. Eukaryotic cells do not have polycistronic mRNAs, raising the question of how they orchestrate the encounter of unassembled subunits. Here we review the constraints and mechanisms governing eukaryotic co- and posttranslational protein folding and assembly, including the influence of elongation rate on nascent chain targeting, folding, and chaperone interactions. Recent evidence shows that mRNAs encoding subunits of oligomeric assemblies can undergo localized translation and form cytoplasmic condensates that might facilitate the assembly of protein complexes. Understanding the interplay between localized mRNA translation and cotranslational proteostasis will be critical to defining protein complex assembly in vivo.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11040709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9769322","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

Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond. 利用全基因组关联研究数据进行表型因果推断：孟德尔随机化及其他。

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Annual Review of Biomedical Data Science Pub Date : 2022-08-10 Epub Date: 2022-04-01 DOI: 10.1146/annurev-biodatasci-122120-024910

Venexia M Walker, Jie Zheng, Tom R Gaunt, George Davey Smith

{"title":"Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond.","authors":"Venexia M Walker, Jie Zheng, Tom R Gaunt, George Davey Smith","doi":"10.1146/annurev-biodatasci-122120-024910","DOIUrl":"10.1146/annurev-biodatasci-122120-024910","url":null,"abstract":"statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614231/pdf/EMS167448.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10780371","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

Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases. 大数据分析的最佳实践，以解决我们在了解疾病的病因、诊断和预后时存在的性别偏见。

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Annual Review of Biomedical Data Science Pub Date : 2022-08-10 Epub Date: 2022-05-13 DOI: 10.1146/annurev-biodatasci-122120-025806

Su Golder, Karen O'Connor, Yunwen Wang, Robin Stevens, Graciela Gonzalez-Hernandez

{"title":"Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases.","authors":"Su Golder, Karen O'Connor, Yunwen Wang, Robin Stevens, Graciela Gonzalez-Hernandez","doi":"10.1146/annurev-biodatasci-122120-025806","DOIUrl":"10.1146/annurev-biodatasci-122120-025806","url":null,"abstract":"A bias in health research to favor understanding diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature on machine learning or natural language processing (NLP) techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (a) \"women,\" \"men,\" or \"sex\"; (b) \"big data,\" \"artificial intelligence,\" or \"NLP\"; and (c) \"disparities\" or \"differences.\" From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in these studies is disproportionately less than women. Even though artificial intelligence and NLP techniques are widely applied in healthresearch, few studies use them to take advantage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process toward correction is slow. We reflect on best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11524028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142366765","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

Discovering Biological Conflict Systems Through Genome Analysis: Evolutionary Principles and Biochemical Novelty. 通过基因组分析发现生物冲突系统:进化原理和生化新颖性。

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Annual Review of Biomedical Data Science Pub Date : 2022-05-24 DOI: 10.1146/annurev-biodatasci-122220-101119

L. Aravind, L. Iyer, A. M. Burroughs

{"title":"Discovering Biological Conflict Systems Through Genome Analysis: Evolutionary Principles and Biochemical Novelty.","authors":"L. Aravind, L. Iyer, A. M. Burroughs","doi":"10.1146/annurev-biodatasci-122220-101119","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-122220-101119","url":null,"abstract":"Biological replicators, from genes within a genome to whole organisms, are locked in conflicts. Comparative genomics has revealed a staggering diversity of molecular armaments and mechanisms regulating their deployment, collectively termed biological conflict systems. These encompass toxins used in inter- and intraspecific interactions, self/nonself discrimination, antiviral immune mechanisms, and counter-host effectors deployed by viruses and intragenomic selfish elements. These systems possess shared syntactical features in their organizational logic and a set of effectors targeting genetic information flow through the Central Dogma, certain membranes, and key molecules like NAD+. These principles can be exploited to discover new conflict systems through sensitive computational analyses. This has led to significant advances in our understanding of the biology of these systems and furnished new biotechnological reagents for genome editing, sequencing, and beyond. We discuss these advances using specific examples of toxins, restriction-modification, apoptosis, CRISPR/second messenger-regulated systems, and other enigmatic nucleic acid-targeting systems. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42599953","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: 8

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration. 在学习医疗保健系统中开发和实施预测模型:退伍军人健康管理中的传统和人工智能方法。

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Annual Review of Biomedical Data Science Pub Date : 2022-05-24 DOI: 10.1146/annurev-biodatasci-122220-110053

D. Atkins, C. A. Makridis, G. Alterovitz, R. Ramoni, C. Clancy

{"title":"Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration.","authors":"D. Atkins, C. A. Makridis, G. Alterovitz, R. Ramoni, C. Clancy","doi":"10.1146/annurev-biodatasci-122220-110053","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-122220-110053","url":null,"abstract":"Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47617284","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: 2