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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
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2021-01-01 DOI: 10.47297/wspbdswsp2752-630502.20210102
Xiaoyuan Li, Yun Liu, Hongyu Si, Peng Wu, Zhenlong Wang
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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
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2021-01-01 DOI: 10.47297/wspbdswsp2752-630503.20210102
Tong Li, Xing Yu, Mengxiang Chen, Y. Lv, W. Zha
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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。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2020-10-30 DOI: 10.20944/preprints202010.0649.v1
B. Percha
Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry.
电子健康记录(EHR)正成为医疗质量改进、研究和运营的重要数据来源。然而,EHR中包含的许多最有价值的信息仍然隐藏在非结构化文本中。近年来,临床文本挖掘领域发展迅速,从基于规则的方法过渡到机器学习,最近又过渡到深度学习。然而,新方法带来了新的挑战,尤其是对那些新进入该领域的人来说。这篇综述为第一次遇到临床文本挖掘的人(例如,医生研究人员、操作分析团队、其他领域的机器学习科学家)提供了临床文本挖掘概述。虽然不是一项全面的调查,但这篇综述描述了最新技术,特别关注过去几年发展起来的新任务和方法。它还确定了这些显著的技术进步与卫生系统和工业实施的实际情况之间的关键障碍。
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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
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2020-07-20 DOI: 10.1146/annurev-biodatasci-121219-025722
P. Kasson
Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.
传染病研究的范围从分子到全球——从病原体耐药性、毒力和复制的特定机制到世界各地的人、动物和病原体的运动。所有这些研究领域都受到了最近大规模数据源和数据分析的影响。其中一些进步依赖于大多数生物医学数据科学常见的数据或分析方法,而另一些则利用了传染病的独特性,即其传染性。本综述概述了过去几年的主要研究进展,并强调了一些剩余的机会,重点是数据或方法方法,特别是传染病。
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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》。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2020-07-01 Epub Date: 2020-05-04 DOI: 10.1146/annurev-biodatasci-021020-093826
Amit Singer, Fred J Sigworth

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.

单颗粒电子冷冻显微镜(冷冻电镜)是一种日益流行的技术,用于以接近原子的分辨率阐明蛋白质和其他具有重要生物意义的复合物的三维结构。它是一种无需结晶的成像方法,可以捕捉分子的原生状态。在单颗粒冷冻电镜中,三维分子结构需要从单个分子的许多有噪声的二维断层投影中确定,而这些分子的方向和位置都是未知的。高水平的噪声和未知的姿态参数是使重建成为一个具有挑战性的计算问题的两个关键因素。更具挑战性的是,当被成像的单个分子处于不同的构象状态时,如何推断结构的可变性和灵活运动。本综述将讨论通过单粒子低温电子显微镜确定结构的计算方法及其来自统计推断、机器学习和信号处理的指导原则,这些原则在许多其他数据科学应用中也发挥着重要作用。
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引用次数: 0
Immunoinformatics: Predicting Peptide-MHC Binding. 免疫信息学:预测多肽- mhc结合。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2020-07-01 DOI: 10.1146/annurev-biodatasci-021920-100259
Morten Nielsen, Massimo Andreatta, Bjoern Peters, Søren Buus

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.

免疫信息学是一门应用计算机科学方法来研究和模拟免疫系统的学科。免疫信息学解决的一个基本问题是如何理解MHC分子向T细胞递呈抗原的规则,这一过程是对感染和癌症的适应性免疫反应的核心。在个性化医疗的现代时代,建模和预测MHC可以呈现哪些抗原的能力是操纵免疫系统和设计治疗干预策略的关键。由于MHC是多基因和极端多态的,每个个体都拥有一组具有不同肽结合特异性的MHC分子,它们共同呈现出正在进行的蛋白质代谢的独特的个性化肽印记。绘制所有MHC同种异体是一项艰巨的任务,没有强大的生物信息学成分是无法实现的。因此,预测多肽- mhc结合的计算工具已成为T细胞表位发现的大多数管道中必不可少的工具,也是疫苗和癌症研究中不可避免的组成部分。在这里,我们描述了几个这样的工具的发展,从开创性的努力到目前最先进的方法,这些工具可以准确预测所有MHC分子的肽结合,甚至包括那些尚未在实验中表征的分子。
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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
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013328
J. Hériché, S. Alexander, J. Ellenberg
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.
荧光显微镜成像长期以来一直是生物医学研究中基于DNA测序和质谱的组学的补充,但这些方法现在正在融合。一方面,组学方法正在从在大细胞群体中平均的体外方法转向具有单细胞敏感性的原位分子表征工具。另一方面,荧光显微镜成像已经从组织和细胞的形态学描述转向单分子分辨率的定量分子图谱。最近以计算方法为基础的技术发展已经开始模糊成像和组学之间的界限,并使它们的直接相关性和无缝集成成为一种令人兴奋的可能性。随着这一趋势的迅速发展,它将使我们能够创建具有空间和时间背景以及亚细胞分辨率的生物系统的全面分子图谱。实现这一宏伟目标的关键将是新颖的计算方法,并成功应对数据集成和共享以及云计算大数据分析的挑战。
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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
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021321
J. Vamathevan, R. Apweiler, E. Birney
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.
技术进步不断推动生物分子数据的生成和生物信息学基础设施的发展,使数据能够用于科学发现。出现了几种类型的数据管理资源,例如数据沉积数据库、增值数据库或知识库以及生物学驱动的门户。在这篇综述中,我们对这些资源的逐渐演变提供了一个独特的概述,并讨论了在开发中必须考虑的目标和特性。随着基因组学在医疗保健领域的应用越来越多,预计到2022年将测序6000万至5亿个全基因组,生物医学研究基础设施也在发生变化。用于联合访问的系统、便携式工具、提供参考数据和解释工具将使研究人员能够从这些数据中获得最大的好处。协作、协调和数据资源的可持续性是确保生物医学知识管理能够随着技术变化和数据量增长而扩展的关键。
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引用次数: 4
Connectivity Mapping: Methods and Applications 连通性映射:方法与应用
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021211
A. Keenan, Megan L. Wojciechowicz, Zichen Wang, Kathleen M. Jagodnik, S. L. Jenkins, Alexander Lachmann, Avi Ma’ayan
Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.
连通性映射资源由表示系统小分子、疾病、基因或其他形式的扰动后细胞状态变化的特征组成。这样的资源使得能够基于相似性对来自新扰动的签名进行表征;提供许多主题扰动的空间的全局视图;并允许预测扰动的细胞、组织和生物体表型的能力。签名搜索引擎通过查找查询签名和签名数据库之间的连接来实现假设生成。该框架已被用于识别小分子与其靶标之间的联系,发现细胞对扰动的特异性反应以及用小分子逆转疾病表达状态的方法,并预测现有药物的小分子拟态物。这篇综述提供了连接映射资源的历史视角和当前状态,重点关注方法论和社区实现。
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引用次数: 34
Molecular Heterogeneity in Large-Scale Biological Data: Techniques and Applications 大规模生物学数据中的分子异质性:技术与应用
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021339
C. Deng, Timothy P. Daley, G. Brandine, Andrew D. Smith
High-throughput sequencing technologies have evolved at a stellar pace for almost a decade and have greatly advanced our understanding of genome biology. In these sampling-based technologies, there is an important detail that is often overlooked in the analysis of the data and the design of the experiments, specifically that the sampled observations often do not give a representative picture of the underlying population. This has long been recognized as a problem in statistical ecology and in the broader statistics literature. In this review, we discuss the connections between these fields, methodological advances that parallel both the needs and opportunities of large-scale data analysis, and specific applications in modern biology. In the process we describe unique aspects of applying these approaches to sequencing technologies, including sequencing error, population and individual heterogeneity, and the design of experiments.
近十年来,高通量测序技术以惊人的速度发展,极大地促进了我们对基因组生物学的理解。在这些基于抽样的技术中,有一个重要的细节在数据分析和实验设计中经常被忽视,特别是抽样观察结果通常不能给出潜在人群的代表性图像。这早已被认为是统计生态学和更广泛的统计文献中的一个问题。在这篇综述中,我们讨论了这些领域之间的联系,平行于大规模数据分析的需求和机会的方法进展,以及在现代生物学中的具体应用。在此过程中,我们描述了将这些方法应用于测序技术的独特方面,包括测序误差,群体和个体异质性以及实验设计。
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引用次数: 5
期刊
Annual Review of Biomedical Data Science
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