检索结果-内蒙古大学图书馆

IEEE International Conference on Healthcare informatics (ICHI)

作者： Yongkang Xiao Yu Hou Huixue Zhou Gayo Diallo Marcelo Fiszman Julian Wolfson Halil Kilicoglu You Chen Hua Xu William G. Mantyh Rui Zhang Institute for Health Informatics University of Minnesota Minneapolis MN USA Department of Surgery University of Minnesota Minneapolis MN USA INRIA SISTM Team AHeaD -INSERM 1219 Bordeaux Population Health University of Bordeaux France NITES - Núcleo de Inovação e Tecnologia Em Saúde Pontifical Catholic University of Rio de Janeiro Brazil Division of Biostatistics School of Public Health University of Minnesota Minneapolis MN USA School of Information Sciences University of Illinois Urbana-Champaign Champaign IL USA Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN USA Section of Biomedical Informatics and Data Science Yale University New Haven Connecticut USA Department of Neurology University of Minnesota Minneapolis MN USA

Recently, computational drug repurposing has emerged as a promising method for identifying new interventions for diseases. This study predicts novel drugs for Alzheimer's disease (AD) through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement (DS) domain knowledge graph, SuppKG, with semantic triples from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set, and was used to generate the score tables for the link prediction task. According to the results of link prediction, we proposed candidate drugs for AD. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover novel drugs for AD. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

arXiv

引用

arXiv 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Zhang, Yue Wang, Ren Shi, Xiaoshuang Xu, Kaidi Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering. However, a robust and general uncertainty measure for free-form answers has not been well-established in open-ended medical question-answering (QA) tasks, where generative inequality introduces a large number of irrelevant words and sequences within the generated set for uncertainty quantification (UQ), which can lead to biases. This paper introduces Word-Sequence Entropy (WSE), a method that calibrates uncertainty at both the word and sequence levels, considering semantic relevance. WSE quantifies uncertainty in a way that is more closely aligned with the reliability of LLMs during uncertainty quantification (UQ). We compare WSE with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs). Experimental results demonstrate that WSE exhibits superior performance in UQ under two standard criteria for correctness evaluation. Additionally, in terms of real-world medical QA applications, the performance of LLMs is significantly enhanced (e.g., a 6.36% improvement in model accuracy on the COVID-QA dataset) by employing responses with lower uncertainty that are identified by WSE as final answers, without any additional task-specific fine-tuning or architectural modifications. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

SSRN

引用

SSRN 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Yao, Huaxiu Zhang, Yue Wang, Ren Xu, Kaidi Shi, Xiaoshuang Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence interaction systems, particularly in the healthcare engineering domain. However, a general method for quantifying the uncertainty of free-form answers has yet to be established in open-ended medical question-answering (QA) tasks, where irrelevant words and sequences with limited semantic information can be the primary source of uncertainty due to the presence of generative inequality. In this paper, we propose the Word-Sequence Entropy (WSE ), which calibrates the uncertainty proportion at both the word and sequence levels according to the semantic relevance, with greater emphasis placed on keywords and more relevant sequences when performing uncertainty quantification. We compare WSE with 6 baseline methods on 5 free-form medical QA datasets, utilizing 7 "off-the-shelf" large language models (LLMs), and show that WSE exhibits superior performance on accurate uncertainty measurement under two standard criteria for correctness evaluation. Additionally, in terms of the potential for real-world medical QA applications, we achieve a significant enhancement (e.g., +6.36% improvement of model accuracy on the COVID-QA dataset) in the performance of LLMs when employing sequences with lower uncertainty that are identified by WSE as final answers, without requiring additional task-specific fine-tuning or architectural modifications. © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

BEYOND ACETABULAR DYSPLASIA AND PINCER MORPHOLOGY: REFINING HIP OSTEOARTHRITIS RISK ASSESSMENT THROUGH STATISTICAL SHAPE MODELING

引用

Osteoarthritis and Cartilage 2025年 33卷 S243-S244页

作者： Fleur Boel Noortje S. Riedstra Harbeer Ahedi Nigel Arden Myrthe A. van den Berg Michiel M. van Buuren Sita Bierma-Zeinstra Flavia Cicuttini Timothy F. Cootes Kay M. Crossley David Felson Willem Paul Giellis Josh Heerey Graeme Jones Stefan Kluzek Nancy E. Lane Claudia Lindner John A. Lynch Joyce v. Meurs Andrea Mosler Rintje Agricola Department of Orthopaedics and Sports Medicine Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Menzies Institute for Medical Research University of Tasmania Australia Tasmania Australia Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences Botnar Research Centre University of Oxford Oxford United Kingdom Department of General Practice Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Rheumatology Monash Universiyt/Alfred Hopstila Melbourne Victoria Australia Informatics Imaging and Data Science The University of Manchester Manchester United Kingdom La Trobe Sport and Exercise Medicine Research Centre School of Allied Health Human Services and Sport La Trobe University Melbourne Victoria Australia Section of Rheumatology Boston University School of Medicine Boston Massachusetts United States Department of Orthopedics University Medical Center Utrecht Utrecht Netherlands La Trobe Sport and Exercise Medicine Research Centre Melbourne Victoria Australia NIHR Biomedical Research Centre University of Nottingham Nottingham United Kingdom Medicine U.C. Davis Health Hillsborough California United States Centre for Imaging Sciences The University of Manchester Manchester United Kingdom Department of Epidemiology and Biostatistics University of California San Francisco California United States Department of Internal Medicine Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands

来源：评论

学校读者我要写书评

暂无评论

The All of Us data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for biomedical Research

引用

biomedical data science 1000年第1期6卷 443-464页

作者： Kelsey R. Mayo Melissa A. Basford Robert J. Carroll Moira Dillon Heather Fullen Jesse Leung Hiral Master Shimon Rura Lina Sulieman Nan Kennedy Eric Banks David Bernick Asmita Gauchan Lee Lichtenstein Brandy M. Mapes Kayla Marginean Steve L. Nyemba Andrea Ramirez Charissa Rotundo Keri Wolfe Weiyi Xia Romuladus E. Azuine Robert M. Cronin Joshua C. Denny Abel Kho Christopher Lunt Bradley Malin Karthik Natarajan Consuelo H. Wilkins Hua Xu George Hripcsak Dan M. Roden Anthony A. Philippakis David Glazer Paul A. Harris 1Vanderbilt Institute for Clinical and Translational Research Vanderbilt University Medical Center Nashville Tennessee USA 2Deparment of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USA email: paul.a.harris@*** 3Verily Life Sciences South San Francisco California USA 4Data Sciences Platform Broad Institute of Harvard and MIT Cambridge Massachusetts USA 5Broad Institute of Harvard and MIT Cambridge Massachusetts USA 6The All of Us Research Program National Institutes of Health Bethesda Maryland USA 7Vanderbilt University Medical Center Enterprise Cybersecurity Vanderbilt University Medical Center Nashville Tennessee USA 8Department of Internal Medicine The Ohio State University Columbus Ohio USA 9Department of Medicine and Institute for Augmented Intelligence in Medicine Feinberg School of Medicine Northwestern University Chicago Illinois USA 10Department of Biomedical Informatics Columbia University New York NY USA 11Department of Medicine Vanderbilt University Medical Center Nashville Tennessee USA 12Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA 13Department of Pharmacology Vanderbilt University Medical Center Nashville Tennessee USA

The All of Us Research Program's data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.

关键词： precision medicine data ecosystem diversity data privacy data integration electronic health records

来源：评论

学校读者我要写书评

暂无评论

Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature

arXiv

引用

arXiv 2023年

作者： Zhou, Huixue Austin, Robin Lu, Sheng-Chieh Silverman, Greg Zhou, Yuqi Kilicoglu, Halil Xu, Hua Zhang, Rui Institute for Health Informatics University of Minnesota MinneapolisMN United States School of Nursing University of Minnesota MinneapolisMN United States The University of Texas MD Anderson Cancer Center Department of Symptom Research HoustonTX United States Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States Department of Pharmaceutical Care & Health Systems University of Minnesota MinneapolisMN United States School of Information Sciences University of Illinois Urbana-ChampaignIL United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven United States

Objective: Our study aimed to construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to better represent the often underrepresented physical and psychological CIH approaches in standard terminologies. We also intended to apply advanced Natural Language Processing (NLP) models such as Bidirectional Encoder Representations from Transformers (BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their performance against established models like MetaMap and CLAMP. Materials and Methods: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant knowledge bases. The Lexicon encompasses 198 unique concepts with 1090 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS). Additionally, we developed and utilized BERT models and compared their efficiency in CIH named entity recognition to that of other models such as MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover, 75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro average F1-score of 0.90, surpassing other models. Conclusion: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts. © 2023, CC BY-NC-SA.

关键词： Terminology

来源：评论

学校读者我要写书评

暂无评论

Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation

arXiv

引用

arXiv 2023年

作者： Yang, Rui Zeng, Qingcheng You, Keen Qiao, Yujie Huang, Lucas Hsieh, Chia-Chun Rosand, Benjamin Goldwasser, Jeremy Dave, Amisha D. Keenan, Tiarnan D.L. Chew, Emily Y. Radev, Dragomir Lu, Zhiyong Xu, Hua Chen, Qingyu Li, Irene Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biomedical Informatics Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Linguistics Northwestern University EvanstonIL United States Department of Computer Science Yale University New HavenCT United States Yale School of Public Health Yale University New HavenCT United States Yale New Haven Hospital Yale School of Medicine Yale University New HavenCT United States Division of Epidemiology and Clinical Applications National Eye Institute National Institutes of Health BethesdaMD United States National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD United States Section of Biomedical Informatics and Data Science Yale School of Medicine Yale University New HavenCT United States Information Technology Center University of Tokyo Tokyo Japan

This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle evaluates and provides interfaces for the latest pre-trained language models, encompassing four advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. © 2023, CC0.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy

引用

Computer Methods and Programs in Biomedicine 2023年 233卷 107480-107480页

作者： Huang, Chih-Wei Wu, Bethany C.Y. Nguyen, Phung Anh Wang, Hsiao-Han Kao, Chih-Chung Lee, Pei-Chen Rahmanti, Annisa Ristya Hsu, Jason C. Yang, Hsuan-Chia Li, Yu-Chuan Jack Taipei Medical University Taipei Taiwan Taipei Medical University Ringgold standard institution - Center for Simulation in Medical Education Taipei 116 Taiwan National Taiwan University Children and Family Research Center Sponsored by CTBC Charity Foundation Taipei Taiwan Clinical Data Center Office of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TMU Da'an Campus 15 F No. 172-1 Kee lung Road Section 2 Da-an District Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology Wanfang Hospital Taipei Medical University Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Delta Research Center Taipei Taiwan Department of Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan

Background and objective: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. Methods: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. Results: data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p © 2023

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

A damaged-informed lung model for ventilator waveforms

arXiv

引用

arXiv 2020年

作者： Agrawal, Deepak K. Smith, Bradford J. Sottile, Peter D. Albers, David J. Department of Bioengineering University of Colorado Denver Anschutz Medical Campus AuroraCO80045 United States Department of Pediatrics University of Colorado Denver Anschutz Medical Campus AuroraCO80045 United States Division of Pulmonary Sciences and Critical Care Medicine Department of Medicine University of Colorado School of Medicine AuroraCO80045 United States Section of Informatics and Data Science Department of Pediatrics School of Medicine University of Colorado Denver Anschutz Medical Campus AuroraCO80045 United States Department of Bioengineering University of Colorado Denver/Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Engineering Columbia University New YorkNY10032 United States

The acute respiratory distress syndrome (ARDS) is characterized by the acute development of diffuse alveolar damage (DAD) resulting in increased vascular permeability and decreased alveolar gas exchange. Mechanical ventilation is a potentially lifesaving intervention to improve oxygen exchange but has the potential to cause ventilator-induced lung injury (VILI). A general strategy to reduce VILI is to use low tidal volume and low-pressure ventilation, but optimal ventilator settings for an individual patient are difficult for the bedside physician to determine and mortality from ARDS remains unacceptably high. Motivated by the need to minimize VILI, scientists have developed models of varying complexity to understand diseased pulmonary physiology. However, simple models often fail to capture real-world injury while complex models tend to not be estimable with clinical data, limiting the clinical utility of existing models. To address this gap, we present a physiologically anchored data-driven model to better model lung injury. Our approach relies on using clinically relevant features in the ventilator waveform data that contain information about pulmonary physiology, patients-ventilator interaction and ventilator settings. Our lung model can reproduce essential physiology and pathophysiology dynamics of differently damaged lungs for both controlled mouse model data and uncontrolled human ICU data. The estimated parameters values that are correlated with a known measure of lung physiology agree with the observed lung damage. In future endeavors, this model could be used to phenotype ventilator waveforms and serve as a basis for predicting the course of ARDS and improving patient care. © 2020, CC-BY-NC-SA.

关键词： Biological organs

来源：评论

学校读者我要写书评

暂无评论

Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele

引用

Nature communications 2025年第1期16卷 3470-3470页

作者： Zhang, Xinruo Brody, Jennifer A. Graff, Mariaelisa Highland, Heather M. Chami, Nathalie Xu, Hanfei Wang, Zhe Ferrier, Kendra R. Chittoor, Geetha Josyula, Navya Shilpa Meyer, Mariah Gupta, Shreyash Li, Xihao Li, Zilin Allison, Matthew A. Becker, Diane M. Bielak, Lawrence F. Bis, Joshua C. Boorgula, Meher Preethi Bowden, Donald W. Broome, Jai G. Buth, Erin J. Carlson, Christopher S. Chang, Kyong-Mi Chavan, Sameer Chiu, Yen-Feng Chuang, Lee-Ming Conomos, Matthew P. DeMeo, Dawn L. Du, Mengmeng Duggirala, Ravindranath Eng, Celeste Fohner, Alison E. Freedman, Barry I. Garrett, Melanie E. Guo, Xiuqing Haiman, Chris Heavner, Benjamin D. Hidalgo, Bertha Hixson, James E. Ho, Yuk-Lam Hobbs, Brian D. Hu, Donglei Hui, Qin Hwu, Chii-Min Jackson, Rebecca D. Jain, Deepti Kalyani, Rita R. Kardia, Sharon L R Kelly, Tanika N. Lange, Ethan M. LeNoir, Michael Li, Changwei Le Marchand, Loic McDonald, Merry-Lynn N McHugh, Caitlin P. Morrison, Alanna C. Naseri, Take Palmer, Nicholette D. Pankow, James S. Perry, James A. Peters, Ulrike Preuss, Michael H. Rao, D.C. Regan, Elizabeth A. Reupena, Sefuiva M. Roden, Dan M. Rodriguez-Santana, Jose Sitlani, Colleen M. Smith, Jennifer A. Tiwari, Hemant K. Vasan, Ramachandran S. Wang, Zeyuan Weeks, Daniel E. Wessel, Jennifer Wiggins, Kerri L. Wilkens, Lynne R. Wilson, Peter W F Yanek, Lisa R. Yoneda, Zachary T. Zhao, Wei Zöllner, Sebastian Arnett, Donna K. Ashley-Koch, Allison E. Barnes, Kathleen C. Blangero, John Boerwinkle, Eric Burchard, Esteban G. Carson, April P. Chasman, Daniel I. Ida Chen, Yii-Der Curran, Joanne E. Fornage, Myriam Gordeuk, Victor R. He, Jiang Heckbert, Susan R. Hou, Lifang Irvin, Marguerite R. Kooperberg, Charles Minster, Ryan L. Mitchell, Braxton D. Nouraie, Mehdi Psaty, Bruce M. Raffield, Laura M. Reiner, Alexander P. Rich, Stephen S. Rotter, Jerome I. Benjamin Shoemaker, M. Smith, Nicholas L. Taylor, Kent D. Telen, Marilyn J. Weiss, Scott T. Zhang, Yingze Heard-Costa, Nancy Sun, Yan V. Lin, Xihong Cupples, L Adrienne Lange, Leslie A. Liu, Ching-Ti Loos, Ruth J F North, K Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill NC United States Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle WA United States The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York NY USA The Mindich Child Health and Development Institute Icahn School of Medicine at Mount Sinai New York NY USA Department of Biostatistics School of Public Health Boston University Boston MA United States Division of Biomedical Informatics and Personalized Medicine University of Colorado School of Medicine Anschutz Medical Campus Aurora CO United States Population Health Sciences Geisinger Danville PA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Department of Biostatistics Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Genetics School of Medicine University of North Carolina at Chapel Hill Chapel Hill NC United States Biostatistics and Health Data Science Indiana University School of Medicine Indianapolis IN United States School of Mathematics and Statistics and KLAS Northeast Normal University Changchun Jilin China Department of Family Medicine Division of Preventive Medicine University of California San Diego La Jolla CA United States Department of Medicine General Internal Medicine Johns Hopkins University School of Medicine Baltimore MD United States Department of Epidemiology School of Public Health University of Michigan Ann Arbor MI United States Department of Medicine University of Colorado School of Medicine Aurora CO United States Department of Biochemistry Wake Forest School of Medicine Winston-Salem NC United States Department of Biostatistics School of Public Health University of Washington Seattle WA United States Department of

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9), including two secondary signals. Notably, we identified and replicated a novel low-frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine. © 2025. The Author(s).

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：