Annual Review of Biomedical Data Science最新文献_第6页

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。

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Annual Review of Biomedical Data Science Pub Date : 2022-05-13 DOI: 10.1146/annurev-biodatasci-122120-113218

Andrew A. Lin, V. Nimgaonkar, D. Issadore, E. Carpenter

{"title":"Extracellular Vesicle-Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer.","authors":"Andrew A. Lin, V. Nimgaonkar, D. Issadore, E. Carpenter","doi":"10.1146/annurev-biodatasci-122120-113218","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-122120-113218","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49480054","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: 5

Exchange of Human Data Across International Boundaries. 跨越国际边界的人类数据交换。

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Annual Review of Biomedical Data Science Pub Date : 2022-05-10 DOI: 10.1146/annurev-biodatasci-122220-110811

H. Bentzen

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。

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Annual Review of Biomedical Data Science Pub Date : 2022-05-10 DOI: 10.1146/annurev-biodatasci-102521-012018

P. Avdeyev, Jian Zhou

{"title":"Computational Approaches for Understanding Sequence Variation Effects on the 3D Genome Architecture.","authors":"P. Avdeyev, Jian Zhou","doi":"10.1146/annurev-biodatasci-102521-012018","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102521-012018","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48686416","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: 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。

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Annual Review of Biomedical Data Science Pub Date : 2022-05-10 DOI: 10.1146/annurev-biodatasci-122120-030732

L. Cowen, H. Putnam

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。

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Annual Review of Biomedical Data Science Pub Date : 2022-05-04 DOI: 10.1146/annurev-biodatasci-122220-112147

Bian Li, Bowen Jin, J. Capra, W. Bush

{"title":"Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation.","authors":"Bian Li, Bowen Jin, J. Capra, W. Bush","doi":"10.1146/annurev-biodatasci-122220-112147","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-122220-112147","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46663334","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: 0

Functional Characterization of Genetic Variant Effects on Expression. 基因变异对表达影响的功能表征。

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Annual Review of Biomedical Data Science Pub Date : 2022-04-28 DOI: 10.1146/annurev-biodatasci-122120-010010

Elise D. Flynn, T. Lappalainen

{"title":"Functional Characterization of Genetic Variant Effects on Expression.","authors":"Elise D. Flynn, T. Lappalainen","doi":"10.1146/annurev-biodatasci-122120-010010","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-122120-010010","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47338850","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: 5

Open Structural Data in Precision Medicine. 精准医学中的开放结构数据。

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Annual Review of Biomedical Data Science Pub Date : 2022-04-28 DOI: 10.1146/annurev-biodatasci-122220-012951

R. Nussinov, Hyunbum Jang, G. Nir, Chung-Jung Tsai, F. Cheng

{"title":"Open Structural Data in Precision Medicine.","authors":"R. Nussinov, Hyunbum Jang, G. Nir, Chung-Jung Tsai, F. Cheng","doi":"10.1146/annurev-biodatasci-122220-012951","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-122220-012951","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45448847","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: 7

Machine Learning in Chemoinformatics and Medicinal Chemistry. 化学信息学和药物化学中的机器学习。

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Annual Review of Biomedical Data Science Pub Date : 2022-04-19 DOI: 10.1146/annurev-biodatasci-122120-124216

Raquel Rodríguez-Pérez, Filip Miljković, J. Bajorath

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。

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Annual Review of Biomedical Data Science Pub Date : 2022-04-19 DOI: 10.1146/annurev-biodatasci-122120-111413

H. Matthews, G. de Jong, T. Maal, P. Claes

{"title":"Static and Motion Facial Analysis for Craniofacial Assessment and Diagnosing Diseases.","authors":"H. Matthews, G. de Jong, T. Maal, P. Claes","doi":"10.1146/annurev-biodatasci-122120-111413","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-122120-111413","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41479900","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: 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?

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Annual Review of Biomedical Data Science 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

{"title":"The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?","authors":"V. Rajagopal, S. Arumugam, Peter J. Hunter, A. Khadangi, Joshua Chung, Michael Pan","doi":"10.1146/annurev-biodatasci-072018-021246","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-072018-021246","url":null,"abstract":"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.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44647308","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: 5