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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。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013350
Peng Jiang, W. Sellers, X. S. Liu
Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.
尽管癌症研究取得了重大进展,但目前的标准治疗药物无法治愈许多类型的癌症。因此,迫切需要确定更好的预测性生物标志物和治疗方案。传统上,来自假设驱动的研究的见解是癌症生物学和治疗发现的主要力量。最近,在高通量技术突破的催化下,大数据资源的快速增长导致了癌症治疗研究的范式转变。计算方法和基因组学数据的结合已经导致了一些成功的临床应用。在这篇综述中,我们重点介绍了数据驱动的抗癌药物疗效建模方法的最新进展,并介绍了数据科学在癌症治疗研究中的挑战和机遇。
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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?
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BD-01-041718-100001
R. Altman, M. Levitt
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:
我们很高兴为您带来《生物医学数据科学年度评论》的第一卷。它涵盖了一系列生物和医学研究挑战,这些挑战是数据密集型的,重点是创造新的方法来推进生物医学科学的发现。“数据科学”一词描述了与获取(通常是大型)数据集以及注释、清理、组织、存储和分析数据集以提取知识相关的专业知识。它融合了统计学、计算机科学和计算工程的学科。许多人对这个词感到恼火——所有的科学最终都取决于数据,上面列出的许多活动听起来像是工程(关于解决问题),而不是科学(关于发现新知识)。如果“数据科学”不是关于科学的,而形容词“数据”没有特别的含义,为什么这个词会存在?事实上,信息学的相关领域已经以多种形式存在了几十年——医学信息学、临床信息学、健康信息学、生物信息学和生物医学信息学——而变体都指的是在生物学和医学领域内分析数据、信息和知识的方法的发展。这些领域的从业者很快指出,如果不是全部的话,大多数数据科学都属于信息学的范畴。信息学是一个广泛的领域,包括与数据、信息和知识互动的社会方面;人机界面的挑战;以及将破坏性的新计算干预引入具有现有工作流程的系统(如医院和实验室)的相关问题。那么,为什么有必要为该领域引入一个新名称呢?“数据科学”一词已经得到了认可,人们对它的普遍认同表明它有着有用的用途。在这里,我们对这个名字在许多活动中的不同使用提出了一些看法:
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引用次数: 8
Alignment-Free Sequence Analysis and Applications. 无配位序列分析及应用。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 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

Genome and metagenome comparisons based on large amounts of next generation sequencing (NGS) data pose significant challenges for alignment-based approaches due to the huge data size and the relatively short length of the reads. Alignment-free approaches based on the counts of word patterns in NGS data do not depend on the complete genome and are generally computationally efficient. Thus, they contribute significantly to genome and metagenome comparison. Recently, novel statistical approaches have been developed for the comparison of both long and shotgun sequences. These approaches have been applied to many problems including the comparison of gene regulatory regions, genome sequences, metagenomes, binning contigs in metagenomic data, identification of virus-host interactions, and detection of horizontal gene transfers. We provide an updated review of these applications and other related developments of word-count based approaches for alignment-free sequence analysis.

基于大量新一代测序(NGS)数据的基因组和元基因组比较对基于比对的方法提出了巨大挑战,因为数据量巨大,读数长度相对较短。基于 NGS 数据中字模式计数的无比对方法不依赖于完整的基因组,通常计算效率较高。因此,它们对基因组和元基因组比较有很大的帮助。最近,人们开发了新的统计方法来比较长序列和霰弹枪序列。这些方法已被应用于许多问题,包括基因调控区、基因组序列、元基因组的比较,元基因组数据中等位基因的分选,病毒-宿主相互作用的鉴定,以及水平基因转移的检测。我们将对这些应用以及基于字数的无比对序列分析方法的其他相关发展进行最新综述。
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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。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 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

With the widespread adoption of electronic health records (EHRs), large repositories of structured and unstructured patient data are becoming available to conduct observational studies. Finding patients with specific conditions or outcomes, known as phenotyping, is one of the most fundamental research problems encountered when using these new EHR data. Phenotyping forms the basis of translational research, comparative effectiveness studies, clinical decision support, and population health analyses using routinely collected EHR data. We review the evolution of electronic phenotyping, from the early rule-based methods to the cutting edge of supervised and unsupervised machine learning models. We aim to cover the most influential papers in commensurate detail, with a focus on both methodology and implementation. Finally, future research directions are explored.

随着电子健康记录(EHR)的广泛采用,结构化和非结构化患者数据的大型存储库正可用于进行观察性研究。在使用这些新的EHR数据时,发现具有特定条件或结果的患者,即表型,是遇到的最基本的研究问题之一。表型是转化研究、比较有效性研究、临床决策支持和使用常规收集的EHR数据进行人群健康分析的基础。我们回顾了电子表型的演变,从早期的基于规则的方法到有监督和无监督机器学习模型的前沿。我们的目标是详细报道最具影响力的文件,重点关注方法和执行。最后,对未来的研究方向进行了探讨。
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引用次数: 125
Defining Phenotypes from Clinical Data to Drive Genomic Research. 从临床数据中定义表型以驱动基因组研究。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2018-07-01 Epub Date: 2018-04-25 DOI: 10.1146/annurev-biodatasci-080917-013335
Jamie R Robinson, Wei-Qi Wei, Dan M Roden, Joshua C Denny

The rise in available longitudinal patient information in electronic health records (EHRs) and their coupling to DNA biobanks has resulted in a dramatic increase in genomic research using EHR data for phenotypic information. EHRs have the benefit of providing a deep and broad data source of health-related phenotypes, including drug response traits, expanding the phenome available to researchers for discovery. The earliest efforts at repurposing EHR data for research involved manual chart review of limited numbers of patients but now typically involve applications of rule-based and machine learning algorithms operating on sometimes huge corpora for both genome-wide and phenome-wide approaches. We highlight here the current methods, impact, challenges, and opportunities for repurposing clinical data to define patient phenotypes for genomics discovery. Use of EHR data has proven a powerful method for elucidation of genomic influences on diseases, traits, and drug-response phenotypes and will continue to have increasing applications in large cohort studies.

电子健康记录(EHRs)中可用的纵向患者信息的增加及其与DNA生物库的耦合导致了使用电子健康记录数据进行表型信息的基因组研究的急剧增加。电子病历的好处是提供了与健康相关的表型(包括药物反应特征)的深入和广泛的数据源,扩大了研究人员可用于发现的表型。将EHR数据重新用于研究的最早努力涉及对有限数量的患者进行手动图表审查,但现在通常涉及基于规则和机器学习算法的应用,这些算法有时用于全基因组和全现象方法的巨大语料库。我们在这里强调当前的方法,影响,挑战和机会,重新利用临床数据来定义基因组学发现的患者表型。电子病历数据的使用已被证明是阐明基因组对疾病、性状和药物反应表型影响的有力方法,并将继续在大型队列研究中得到越来越多的应用。
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引用次数: 26
Privacy Policy and Technology in Biomedical Data Science. 生物医学数据科学中的隐私政策与技术。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2018-07-01 DOI: 10.1146/annurev-biodatasci-080917-013416
April Moreno Arellano, Wenrui Dai, Shuang Wang, Xiaoqian Jiang, Lucila Ohno-Machado

Privacyis an important consideration when sharing clinical data, which often contain sensitive information. Adequate protection to safeguard patient privacy and to increase public trust in biomedical research is paramount. This review covers topics in policy and technology in the context of clinical data sharing. We review policy articles related to (a) the Common Rule, HIPAA privacy and security rules, and governance; (b) patients' viewpoints and consent practices; and (c) research ethics. We identify key features of the revised Common Rule and the most notable changes since its previous version. We address data governance for research in addition to the increasing emphasis on ethical and social implications. Research ethics topics include data sharing best practices, use of data from populations of low socioeconomic status (SES), recent updates to institutional review board (IRB) processes to protect human subjects' data, and important concerns about the limitations of current policies to address data deidentification. In terms of technology, we focus on articles that have applicability in real world health care applications: deidentification methods that comply with HIPAA, data anonymization approaches to satisfy well-acknowledged issues in deidentified data, encryption methods to safeguard data analyses, and privacy-preserving predictive modeling. The first two technology topics are mostly relevant to methodologies that attempt to sanitize structured or unstructured data. The third topic includes analysis on encrypted data. The last topic includes various mechanisms to build statistical models without sharing raw data.

隐私是共享临床数据时的一个重要考虑因素,这些数据通常包含敏感信息。充分保护患者隐私和增加公众对生物医学研究的信任至关重要。这篇综述涵盖了临床数据共享背景下的政策和技术主题。我们审查了与(a)通用规则、HIPAA隐私和安全规则以及治理相关的政策文章;(b) 患者的观点和同意做法;以及(c)研究伦理。我们确定了修订后的共同规则的主要特点以及自上一版本以来最显著的变化。除了越来越重视伦理和社会影响外,我们还致力于研究数据治理。研究伦理主题包括数据共享最佳实践、低社会经济地位人群(SES)数据的使用、机构审查委员会(IRB)保护人类受试者数据程序的最新更新,以及对当前解决数据去识别问题的政策局限性的重要关注。在技术方面,我们专注于在现实世界的医疗保健应用中具有适用性的文章:符合HIPAA的去识别方法,满足去识别数据中公认问题的数据匿名方法,保护数据分析的加密方法,以及保护隐私的预测建模。前两个技术主题主要与试图净化结构化或非结构化数据的方法论有关。第三个主题包括对加密数据的分析。最后一个主题包括在不共享原始数据的情况下构建统计模型的各种机制。
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引用次数: 0
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Annual Review of Biomedical Data Science
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