Annual Review of Biomedical Data Science最新文献_第7页

A Semantic Ontology Structure－based Approach for Re⁃trieving Similar Medical Images 基于语义本体结构的相似医学图像检索方法

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Annual Review of Biomedical Data Science Pub Date : 2021-01-01 DOI: 10.47297/wspbdswsp2752-630501.20210102

Yiwen Wang

引用次数: 0

Alpha to beta cell reprogramming to treat Type 1 diabetes 细胞重编程治疗1型糖尿病

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Annual Review of Biomedical Data Science Pub Date : 2021-01-01 DOI: 10.47297/wspbdswsp2752-630504.20210102

V. Li

引用次数: 0

Establishment of Visual Fear Conditioning in Long－evansRats 长时间迁徙大鼠视觉恐惧条件反射的建立

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Annual Review of Biomedical Data Science Pub Date : 2021-01-01 DOI: 10.47297/wspbdswsp2752-630502.20210102

Xiaoyuan Li, Yun Liu, Hongyu Si, Peng Wu, Zhenlong Wang

引用次数: 0

Genomic Characteristics of SARS-CoV, MERS-CoV and2019-nCoV SARS-CoV、MERS-CoV和2019- ncov的基因组特征

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Annual Review of Biomedical Data Science Pub Date : 2021-01-01 DOI: 10.47297/wspbdswsp2752-630503.20210102

Tong Li, Xing Yu, Mengxiang Chen, Y. Lv, W. Zha

引用次数: 0

Modern Clinical Text Mining: A Guide and Review. 现代临床文本挖掘：指南与综述。

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Annual Review of Biomedical Data Science Pub Date : 2020-10-30 DOI: 10.20944/preprints202010.0649.v1

B. Percha

引用次数: 26

Infectious Disease Research in the Era of Big Data 大数据时代的传染病研究

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Annual Review of Biomedical Data Science Pub Date : 2020-07-20 DOI: 10.1146/annurev-biodatasci-121219-025722

P. Kasson

引用次数: 6

Computational Methods for Single-Particle Electron Cryomicroscopy. 单粒子电子冷冻显微计算方法》。

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Annual Review of Biomedical Data Science Pub Date : 2020-07-01 Epub Date: 2020-05-04 DOI: 10.1146/annurev-biodatasci-021020-093826

Amit Singer, Fred J Sigworth

{"title":"Computational Methods for Single-Particle Electron Cryomicroscopy.","authors":"Amit Singer, Fred J Sigworth","doi":"10.1146/annurev-biodatasci-021020-093826","DOIUrl":"10.1146/annurev-biodatasci-021020-093826","url":null,"abstract":"Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional structure of proteins and other biologically significant complexes at near-atomic resolution. It is an imaging method that does not require crystallization and can capture molecules in their native states. In single-particle cryo-EM, the three-dimensional molecular structure needs to be determined from many noisy two-dimensional tomographic projections of individual molecules, whose orientations and positions are unknown. The high level of noise and the unknown pose parameters are two key elements that make reconstruction a challenging computational problem. Even more challenging is the inference of structural variability and flexible motions when the individual molecules being imaged are in different conformational states. This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing that also play a significant role in many other data science applications.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"3 ","pages":"163-190"},"PeriodicalIF":6.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8412055/pdf/nihms-1580485.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10390679","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

Immunoinformatics: Predicting Peptide-MHC Binding. 免疫信息学:预测多肽- mhc结合。

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Annual Review of Biomedical Data Science Pub Date : 2020-07-01 DOI: 10.1146/annurev-biodatasci-021920-100259

Morten Nielsen, Massimo Andreatta, Bjoern Peters, Søren Buus

{"title":"Immunoinformatics: Predicting Peptide-MHC Binding.","authors":"Morten Nielsen, Massimo Andreatta, Bjoern Peters, Søren Buus","doi":"10.1146/annurev-biodatasci-021920-100259","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-021920-100259","url":null,"abstract":"Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":"3 ","pages":"191-215"},"PeriodicalIF":6.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/annurev-biodatasci-021920-100259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9809194","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: 48

Integrating Imaging and Omics: Computational Methods and Challenges 整合成像和组学:计算方法和挑战

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Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013328

J. Hériché, S. Alexander, J. Ellenberg

{"title":"Integrating Imaging and Omics: Computational Methods and Challenges","authors":"J. Hériché, S. Alexander, J. Ellenberg","doi":"10.1146/ANNUREV-BIODATASCI-080917-013328","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-080917-013328","url":null,"abstract":"Fluorescence microscopy imaging has long been complementary to DNA sequencing- and mass spectrometry–based omics in biomedical research, but these approaches are now converging. On the one hand, omics methods are moving from in vitro methods that average across large cell populations to in situ molecular characterization tools with single-cell sensitivity. On the other hand, fluorescence microscopy imaging has moved from a morphological description of tissues and cells to quantitative molecular profiling with single-molecule resolution. Recent technological developments underpinned by computational methods have started to blur the lines between imaging and omics and have made their direct correlation and seamless integration an exciting possibility. As this trend continues rapidly, it will allow us to create comprehensive molecular profiles of living systems with spatial and temporal context and subcellular resolution. Key to achieving this ambitious goal will be novel computational methods and successfully dealing with the challenges of data integration and sharing as well as cloud-enabled big data analysis.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-080917-013328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43681862","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: 27

Biomolecular Data Resources: Bioinformatics Infrastructure for Biomedical Data Science 生物分子数据资源：生物医学数据科学的生物信息学基础设施

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Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021321

J. Vamathevan, R. Apweiler, E. Birney

{"title":"Biomolecular Data Resources: Bioinformatics Infrastructure for Biomedical Data Science","authors":"J. Vamathevan, R. Apweiler, E. Birney","doi":"10.1146/ANNUREV-BIODATASCI-072018-021321","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-072018-021321","url":null,"abstract":"Technological advances have continuously driven the generation of bio-molecular data and the development of bioinformatics infrastructure, which enables data reuse for scientific discovery. Several types of data management resources have arisen, such as data deposition databases, added-value databases or knowledgebases, and biology-driven portals. In this review, we provide a unique overview of the gradual evolution of these resources and discuss the goals and features that must be considered in their development. With the increasing application of genomics in the health care context and with 60 to 500 million whole genomes estimated to be sequenced by 2022, biomedical research infrastructure is transforming, too. Systems for federated access, portable tools, provision of reference data, and interpretation tools will enable researchers to derive maximal benefits from these data. Collaboration, coordination, and sustainability of data resources are key to ensure that biomedical knowledge management can scale with technology shifts and growing data volumes.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-072018-021321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45228710","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: 4