Annual Review of Biomedical Data Science最新文献_第8页

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

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。

IF 6

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":" ","pages":""},"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。

IF 6

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":" ","pages":""},"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。

IF 6

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":" ","pages":""},"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。

IF 6

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。

IF 6

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":"1 1","pages":""},"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?

IF 6

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":" ","pages":""},"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

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases 大数据分析的最佳实践，以解决我们对疾病病因、诊断和预后的理解中的性别特异性偏差

IF 6

Annual Review of Biomedical Data Science Pub Date : 2022-02-06 DOI: 10.1101/2022.01.31.22270183

S. Golder, K. O’Connor, Yunwen Wang, R. Stevens, G. Gonzalez-Hernandez

{"title":"Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases","authors":"S. Golder, K. O’Connor, Yunwen Wang, R. Stevens, G. Gonzalez-Hernandez","doi":"10.1101/2022.01.31.22270183","DOIUrl":"https://doi.org/10.1101/2022.01.31.22270183","url":null,"abstract":"A bias in health research to favor understanding of diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature that used machine learning or NLP techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (1) \"women\" or \"men\" or \"sex,\" (2) \"big data\" or \"artificial intelligence\" or \"NLP\", and (3) \"disparities\" or \"differences.\" From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in the included studies is disproportionately less than women. Even though AI and NLP techniques are widely applied in health research, few studies use them to take advatage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process to- wards correction is slow. We reflected on what would be the best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44284431","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

Single-Cell Analysis for Whole-Organism Datasets. 全生物数据集的单细胞分析。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2021-07-20 Epub Date: 2021-05-11 DOI: 10.1146/annurev-biodatasci-092820-031008

Angela Oliveira Pisco, Bruno Tojo, Aaron McGeever

引用次数: 4

The 3D Genome Structure of Single Cells. 单细胞的三维基因组结构。

IF 6

Annual Review of Biomedical Data Science Pub Date : 2021-07-20 Epub Date: 2021-04-23 DOI: 10.1146/annurev-biodatasci-020121-084709

Tianming Zhou, Ruochi Zhang, Jian Ma

引用次数: 29