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Extracellular Vesicle-Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer. 基于细胞外囊泡的多分析物液体活检诊断癌症。
IF 6 Pub Date : 2022-05-13 DOI: 10.1146/annurev-biodatasci-122120-113218
Andrew A. Lin, V. Nimgaonkar, D. Issadore, E. Carpenter
Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation. 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.
液体活检是分析肿瘤进入循环的物质,如循环肿瘤细胞、核酸和细胞外囊泡(EVs),用于癌症的诊断和管理。随着美国食品药品监督管理局最近批准晚期转移性疾病患者使用单一生物标志物,这些检测方法迅速发展。然而,它们作为早期癌症的诊断缺乏敏感性或特异性,主要是由于循环血浆中的低浓度。EVs是一种由肿瘤和其他细胞脱落到循环中的膜包裹的纳米级囊泡,是一种很有前途的液体活检分析物,因为它们的蛋白质和核酸货物是从其母细胞携带的,它们的表面蛋白是其来源细胞特异性的,并且在整个疾病阶段的浓度高于其他非侵入性生物标志物。最近,电动汽车与非电动汽车生物标志物的结合推动了灵敏度和准确性的提高;这是由使用机器学习(ML)以算法识别多种生物标志物并将其组合成用于临床预测的复合生物标志物所推动的。这篇综述分析了EV分离方法,调查了在多分析细胞EV数据集中使用ML的方法和问题,并描述了将多分析细胞液体活检应用于临床的最佳实践。《生物医学数据科学年度评论》第5卷预计最终在线出版日期为2022年8月。请参阅http://www.annualreviews.org/page/journal/pubdates用于修订估算。
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引用次数: 5
Exchange of Human Data Across International Boundaries. 跨越国际边界的人类数据交换。
IF 6 Pub Date : 2022-05-10 DOI: 10.1146/annurev-biodatasci-122220-110811
H. Bentzen
There is a need to share personal data across jurisdictional boundaries. However, the laws regulating such transfers are not harmonized, and sometimes even conflict, causing challenges and occasional data stalls. This review describes the legal landscape for transfer of human data across international boundaries. The European Union's data protection legislation is used as the starting point for illustrating the legislation of countries across the world, how these diverge, and one's options for exchanging human data internationally in a legally compliant manner. 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.
有必要跨司法管辖区共享个人数据。然而,规范此类传输的法律并不统一,有时甚至会发生冲突,造成挑战和偶尔的数据停滞。这篇综述描述了跨越国际边界转移人类数据的法律环境。欧盟的数据保护立法被用作说明世界各国立法的起点,这些立法是如何分歧的,以及以合法的方式在国际上交换人类数据的选择。《生物医学数据科学年度评论》第5卷预计最终在线出版日期为2022年8月。请参阅http://www.annualreviews.org/page/journal/pubdates用于修订估算。
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引用次数: 1
Computational Approaches for Understanding Sequence Variation Effects on the 3D Genome Architecture. 理解序列变异对三维基因组结构影响的计算方法。
IF 6 Pub Date : 2022-05-10 DOI: 10.1146/annurev-biodatasci-102521-012018
P. Avdeyev, Jian Zhou
Decoding how genomic sequence and its variations affect 3D genome architecture is indispensable for understanding the genetic architecture of various traits and diseases. The 3D genome organization can be significantly altered by genome variations and in turn impact the function of the genomic sequence. Techniques for measuring the 3D genome architecture across spatial scales have opened up new possibilities for understanding how the 3D genome depends upon the genomic sequence and how it can be altered by sequence variations. Computational methods have become instrumental in analyzing and modeling the sequence effects on 3D genome architecture, and recent development in deep learning sequence models have opened up new opportunities for studying the interplay between sequence variations and the 3D genome. In this review, we focus on computational approaches for both the detection and modeling of sequence variation effects on the 3D genome, and we discuss the opportunities presented by these approaches. 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.
解码基因组序列及其变异如何影响三维基因组结构对于理解各种性状和疾病的遗传结构是必不可少的。三维基因组组织可以通过基因组变异显著改变,进而影响基因组序列的功能。跨空间尺度测量三维基因组结构的技术为理解三维基因组如何依赖于基因组序列以及序列变化如何改变基因组结构开辟了新的可能性。计算方法已经成为分析和建模序列对三维基因组结构影响的工具,而深度学习序列模型的最新发展为研究序列变异与三维基因组之间的相互作用开辟了新的机会。在这篇综述中,我们重点介绍了序列变异对三维基因组影响的检测和建模的计算方法,并讨论了这些方法所带来的机会。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 1
Bioinformatics of Corals: Investigating Heterogeneous Omics Data from Coral Holobionts for Insight into Reef Health and Resilience. 珊瑚礁生物信息学:研究珊瑚礁中的异构奥密克戎数据,深入了解珊瑚礁的健康和复原力。
IF 6 Pub Date : 2022-05-10 DOI: 10.1146/annurev-biodatasci-122120-030732
L. Cowen, H. Putnam
Coral reefs are home to over two million species and provide habitat for roughly 25% of all marine animals, but they are being severely threatened by pollution and climate change. A large amount of genomic, transcriptomic, and other omics data is becoming increasingly available from different species of reef-building corals, the unicellular dinoflagellates, and the coral microbiome (bacteria, archaea, viruses, fungi, etc.). Such new data present an opportunity for bioinformatics researchers and computational biologists to contribute to a timely, compelling, and urgent investigation of critical factors that influence reef health and resilience. 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.
珊瑚礁是200多万种物种的家园,为大约25%的海洋动物提供栖息地,但它们正受到污染和气候变化的严重威胁。从不同种类的造礁珊瑚、单细胞鞭毛藻和珊瑚微生物组(细菌、古细菌、病毒、真菌等)中获得的大量基因组学、转录组学和其他组学数据越来越多。这些新数据为生物信息学研究人员和计算生物学家提供了一个机会,可以对影响珊瑚礁健康和恢复力的关键因素进行及时、有说服力和紧急的调查。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 3
Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation. 整合蛋白质结构和群体规模的DNA序列数据,用于疾病基因发现和变异解释。
IF 6 Pub Date : 2022-05-04 DOI: 10.1146/annurev-biodatasci-122220-112147
Bian Li, Bowen Jin, J. Capra, W. Bush
The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. 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.
在过去的20年里,用于获取人类基因组中蛋白质结构和遗传变异信息的实验和计算技术取得了巨大进步,产生了大量新的数据资源。在这篇综述中,我们讨论了这些进展,以及确定遗传变异对蛋白质功能影响的新方法。我们专注于将人类遗传变异整合到蛋白质结构中以发现疾病关系的新方法的潜力,包括发现癌症相关蛋白质的突变热点,在常见复杂疾病的蛋白质区域内定位蛋白质改变变异,以及评估孟德尔性状的未知意义变异。我们期望整合这些数据源的方法将在疾病基因发现和变异解释中发挥越来越重要的作用。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 0
Functional Characterization of Genetic Variant Effects on Expression. 基因变异对表达影响的功能表征。
IF 6 Pub Date : 2022-04-28 DOI: 10.1146/annurev-biodatasci-122120-010010
Elise D. Flynn, T. Lappalainen
Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. 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.
通过全基因组关联研究(GWAS),人类中数千种常见的遗传变异与疾病风险和表型变异有关。然而,大多数GWAS变体属于基因组的非编码区,这使我们对其调控功能的理解变得复杂,而且很少有GWAS变体效应的分子机制得到明确阐明。在这里,我们开始综述遗传变异效应,重点关注表达数量性状基因座(eQTL),包括它们在解释GWAS变异机制中的作用。我们讨论了eQTL分析的相关挑战和机遇,包括确定因果变异、阐明分子作用机制和理解上下文变异。解决这些问题可以更好地表征疾病相关基因座的功能,并深入了解非编码遗传调控密码及其对基因表达的控制的基本生物学问题。《生物医学数据科学年度评论》第5卷预计最终在线出版日期为2022年8月。请参阅http://www.annualreviews.org/page/journal/pubdates用于修订估算。
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引用次数: 5
Open Structural Data in Precision Medicine. 精准医学中的开放结构数据。
IF 6 Pub Date : 2022-04-28 DOI: 10.1146/annurev-biodatasci-122220-012951
R. Nussinov, Hyunbum Jang, G. Nir, Chung-Jung Tsai, F. Cheng
Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. 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.
在分子水平上的三维蛋白质结构数据是成功的精准医疗的关键。这些数据不仅对于发现阻断目标突变蛋白活性位点的药物至关重要,而且对于向患者和临床医生阐明患者所携带的突变如何起作用至关重要。结构数据的相对缺乏反映了它们的成本、解释上的挑战以及缺乏临床应用指南。实验高分辨率结构测定技术的快速发展越来越多地产生结构。在计算方面,包括分子动力学模拟在内的建模算法正变得越来越强大,计算密集型硬件,特别是图形处理单元,也随着百亿亿次时代的开始而变得越来越强大。可访问的、免费的、详细的结构和动态数据可以与大数据相结合,有力地改变个性化药理学。在这里,我们回顾了蛋白质和新兴的基因组高分辨率数据,以及方法、应用和例子,强调了它们在精准医学中的有用性。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 7
Machine Learning in Chemoinformatics and Medicinal Chemistry. 化学信息学和药物化学中的机器学习。
IF 6 Pub Date : 2022-04-19 DOI: 10.1146/annurev-biodatasci-122120-124216
Raquel Rodríguez-Pérez, Filip Miljković, J. Bajorath
In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, and reaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning and deep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains. 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.
在化学信息学和药物化学中,机器学习已经发展成为一种重要的方法。近年来,越来越多的计算资源和新的深度学习算法将机器学习提升到一个新的水平,解决了制药研究中以前未遇到的挑战。随着新算法的发展和大数据的出现,化合物活性预测、从头设计和反应建模的计算机方法得到了进一步的发展。本文综述了机器学习和深度学习在化学信息学和药物化学中的新应用。讨论了新方法和应用程序的机遇和挑战,重点放在适当的基线比较,稳健的验证方法和新的适用领域。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 6
Static and Motion Facial Analysis for Craniofacial Assessment and Diagnosing Diseases. 静态和运动面部分析用于颅面评估和疾病诊断。
IF 6 Pub Date : 2022-04-19 DOI: 10.1146/annurev-biodatasci-122120-111413
H. Matthews, G. de Jong, T. Maal, P. Claes
Deviation from a normal facial shape and symmetry can arise from numerous sources, including physical injury and congenital birth defects. Such abnormalities can have important aesthetic and functional consequences. Furthermore, in clinical genetics distinctive facial appearances are often associated with clinical or genetic diagnoses; the recognition of a characteristic facial appearance can substantially narrow the search space of potential diagnoses for the clinician. Unusual patterns of facial movement and expression can indicate disturbances to normal mechanical functioning or emotional affect. Computational analyses of static and moving 2D and 3D images can serve clinicians and researchers by detecting and describing facial structural, mechanical, and affective abnormalities objectively. In this review we survey traditional and emerging methods of facial analysis, including statistical shape modeling, syndrome classification, modeling clinical face phenotype spaces, and analysis of facial motion and affect. 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.
与正常面部形状和对称性的偏差可能来自多种原因,包括身体损伤和先天性出生缺陷。这种异常可能会产生重要的美学和功能后果。此外,在临床遗传学中,独特的面部外观通常与临床或遗传诊断有关;特征面部外观的识别可以显著地缩小临床医生潜在诊断的搜索空间。面部运动和表情的异常模式可能表明正常的机械功能或情绪受到干扰。静态和运动2D和3D图像的计算分析可以通过客观地检测和描述面部结构、机械和情感异常来为临床医生和研究人员提供服务。在这篇综述中,我们综述了传统和新兴的面部分析方法,包括统计形状建模、综合征分类、临床面部表型空间建模以及面部运动和情感分析。《生物医学数据科学年度评论》第5卷预计最终在线出版日期为2022年8月。请参阅http://www.annualreviews.org/page/journal/pubdates用于修订估算。
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引用次数: 3
The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology? 细胞重组:我们在预测单细胞生物学的计算生理学框架中需要什么?
IF 6 Pub Date : 2022-02-27 DOI: 10.1146/annurev-biodatasci-072018-021246
V. Rajagopal, S. Arumugam, Peter J. Hunter, A. Khadangi, Joshua Chung, Michael Pan
Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science. 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.
现代生物学和生物医学正在经历大数据爆炸,需要先进的计算算法来提取活细胞生理状态的机制见解。我们提出了细胞生理组项目的动机:创建、共享和使用基于生物物理学的单细胞生理学计算模型的框架和方法。以钙信号传导、生物能量学和内体运输为例,我们强调需要空间详细的、基于生物物理学的计算模型来揭示细胞生物学的新机制。我们回顾了迄今为止在创建细胞生理组模型方面的进展和挑战。然后,我们引入键合图作为创建细胞生理组模型的有效方法,该模型集成了化学、机械、电磁和热过程,同时保持质量和能量平衡。键合图增强了细胞计算模型的模块化和可重用性。最后,我们展望了将有助于充分实现这一令人兴奋的机械生物医学数据科学新领域的步骤。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 5
期刊
Annual Review of Biomedical Data Science
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