Annual Review of Biomedical Data Science最新文献_第9页

Large-Scale Analysis of Genetic and Clinical Patient Data 遗传和临床患者数据的大规模分析

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013508

M. Ritchie

{"title":"Large-Scale Analysis of Genetic and Clinical Patient Data","authors":"M. Ritchie","doi":"10.1146/ANNUREV-BIODATASCI-080917-013508","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-080917-013508","url":null,"abstract":"Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2018-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-080917-013508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46186041","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: 13

From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture 从组织到细胞类型再返回:组织结构的单细胞基因表达分析

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013452

Xi Chen, S. Teichmann, K. Meyer

引用次数: 77

Deep Learning in Biomedical Data Science 生物医学数据科学中的深度学习

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013343

P. Baldi

引用次数: 77

Network Analysis as a Grand Unifier in Biomedical Data Science 网络分析是生物医学数据科学的一大统一体

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013444

Patrick D. McGillivray, Declan Clarke, W. Meyerson, Jing Zhang, Donghoon Lee, Mengting Gu, Sushant Kumar, Holly Zhou, M. Gerstein

{"title":"Network Analysis as a Grand Unifier in Biomedical Data Science","authors":"Patrick D. McGillivray, Declan Clarke, W. Meyerson, Jing Zhang, Donghoon Lee, Mengting Gu, Sushant Kumar, Holly Zhou, M. Gerstein","doi":"10.1146/ANNUREV-BIODATASCI-080917-013444","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-080917-013444","url":null,"abstract":"Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2018-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-080917-013444","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49037025","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: 28

A Census of Disease Ontologies 疾病本体论普查

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013459

M. Haendel, J. McMurry, R. Relevo, C. Mungall, P. Robinson, C. Chute

引用次数: 35

Data Science Issues in Studying Protein-RNA Interactions with CLIP Technologies. 利用 CLIP 技术研究蛋白质-RNA 相互作用的数据科学问题。

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/annurev-biodatasci-080917-013525

Anob M Chakrabarti, Nejc Haberman, Arne Praznik, Nicholas M Luscombe, Jernej Ule

{"title":"Data Science Issues in Studying Protein-RNA Interactions with CLIP Technologies.","authors":"Anob M Chakrabarti, Nejc Haberman, Arne Praznik, Nicholas M Luscombe, Jernej Ule","doi":"10.1146/annurev-biodatasci-080917-013525","DOIUrl":"10.1146/annurev-biodatasci-080917-013525","url":null,"abstract":"<p><p>An interplay of experimental and computational methods is required to achieve a comprehensive understanding of protein-RNA interactions. UV crosslinking and immunoprecipitation (CLIP) identifies endogenous interactions by sequencing RNA fragments that copurify with a selected RNA-binding protein under stringent conditions. Here we focus on approaches for the analysis of the resulting data and appraise the methods for peak calling, visualization, analysis, and computational modeling of protein-RNA binding sites. We advocate that the sensitivity and specificity of data be assessed in combination for computational quality control. Moreover, we demonstrate the value of analyzing sequence motif enrichment in peaks assigned from CLIP data and of visualizing RNA maps, which examine the positional distribution of peaks around regulated landmarks in transcripts. We use these to assess how variations in CLIP data quality and in different peak calling methods affect the insights into regulatory mechanisms. We conclude by discussing future opportunities for the computational analysis of protein-RNA interaction experiments.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"1 1","pages":"235-261"},"PeriodicalIF":6.0,"publicationDate":"2018-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614488/pdf/EMS174063.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9404672","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

Big Data Approaches for Modeling Response and Resistance to Cancer Drugs. 癌症药物反应和耐药性建模的大数据方法。

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013350

Peng Jiang, W. Sellers, X. S. Liu

引用次数: 23

What is Biomedical Data Science and Do We Need an Annual Review of It? 什么是生物医学数据科学？我们需要对其进行年度审查吗？

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BD-01-041718-100001

R. Altman, M. Levitt

{"title":"What is Biomedical Data Science and Do We Need an Annual Review of It?","authors":"R. Altman, M. Levitt","doi":"10.1146/ANNUREV-BD-01-041718-100001","DOIUrl":"https://doi.org/10.1146/ANNUREV-BD-01-041718-100001","url":null,"abstract":"We are pleased to bring you the first volume of the Annual Review of Biomedical Data Science. It spans a range of biological and medical research challenges that are data intensive and focused on the creation of novel methodologies to advance biomedical science discovery. The term “data science” describes expertise associated with taking (usually large) data sets and annotating, cleaning, organizing, storing, and analyzing them for the purposes of extracting knowledge. It merges the disciplines of statistics, computer science, and computational engineering. Many are irritated by the term—all of science depends ultimately on data, and many of the activities listed above sound like engineering (which is about solving problems) and not science (which is about discovery of new knowledge). If “data science” is not about science and the adjective “data” has no particular meaning, why does this term exist? Indeed, the allied fields of informatics have existed for several decades in many forms—medical informatics, clinical informatics, health informatics, bioinformatics, and biomedical informatics—and variants all refer to the development of methods to analyze data, information, and knowledge within the space of biology and medicine. Practitioners of these fields are quick to point out that most if not all of data science falls within the purview of informatics. Informatics is a broad field that includes the social aspects of interacting with data, information, and knowledge; the challenges of human–computer interfaces; and the issues associated with introducing disruptive new computational interventions into systems (like hospitals and laboratories) with existing workflows. So why is the introduction of a new name for the field necessary? The term “data science” has gained recognition, and the widespread comfort with it suggests it serves a useful purpose. Here we offer some observations on the diverse use of the moniker for many activities:","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2018-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BD-01-041718-100001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41873602","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

Alignment-Free Sequence Analysis and Applications. 无配位序列分析及应用。

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Annual Review of Biomedical Data Science Pub Date : 2018-07-01 Epub Date: 2018-04-25 DOI: 10.1146/annurev-biodatasci-080917-013431

Jie Ren, Xin Bai, Yang Young Lu, Kujin Tang, Ying Wang, Gesine Reinert, Fengzhu Sun

引用次数: 0

Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models. 电子表型研究进展：从基于规则的定义到机器学习模型。

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Annual Review of Biomedical Data Science Pub Date : 2018-07-01 Epub Date: 2018-05-23 DOI: 10.1146/annurev-biodatasci-080917-013315

Juan M Banda, Martin Seneviratne, Tina Hernandez-Boussard, Nigam H Shah

引用次数: 125