检索结果-内蒙古大学图书馆

Correction: Profiling chromatin accessibility in pediatric acute lymphoblastic leukemia identifies subtype-specific chromatin landscapes and gene regulatory networks

引用

Leukemia 2024年第8期38卷 1867页

作者： Jonathan D Diedrich Qian Dong Daniel C Ferguson Brennan P Bergeron Robert J Autry Maoxiang Qian Wenjian Yang Colton Smith James B Papizan Jon P Connelly Kohei Hagiwara Kristine R Crews Shondra M Pruett-Miller Ching-Hon Pui Jun J Yang Mary V Relling William E Evans Daniel Savic Hematological Malignancies Program and Center for Precision Medicine in Leukemia St. Jude Children's Research Hospital Memphis TN USA. Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN USA. Graduate School of Biomedical Sciences St. Jude Children's Research Hospital Memphis TN USA. Integrated Biomedical Sciences Program University of Tennessee Health Science Center Memphis TN USA. Department of Cell and Molecular biology and Center for Advanced Genome Engineering St. Jude Children's Research Hospital Memphis TN USA. Department of Computational Biology St. Jude Children's Research Hospital Memphis TN USA. Department of Oncology St. Jude Children's Research Hospital Memphis TN USA. Department of Pathology St. Jude Children's Research Hospital Memphis TN USA. Hematological Malignancies Program and Center for Precision Medicine in Leukemia St. Jude Children's Research Hospital Memphis TN USA. daniel.savic@***. Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN USA. daniel.savic@***. Integrated Biomedical Sciences Program University of Tennessee Health Science Center Memphis TN USA. daniel.savic@***.

来源：评论

学校读者我要写书评

暂无评论

Dynamic ¹³C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin (vol 23, 100855, 2020)

引用

Iscience 2020年第12期23卷 100855页

作者： Quek, Lake-Ee Krycer, James R. Ohno, Satoshi Yugi, Katsuyuki Fazakerley, Daniel J. Scalzo, Richard Elkington, Sarah D. Dai, Ziwei Hirayama, Akiyoshi Ikeda, Satsuki Shoji, Futaba Suzuki, Kumi Locasale, Jason W. Soga, Tomoyoshi James, David E. Kuroda, Shinya Charles Perkins Centre The University of Sydney Sydney NSW 2006 Australia School of Mathematics and Statistics The University of Sydney Sydney NSW 2006 Australia Sydney Medical School The University of Sydney Sydney NSW 2006 Australia Department of Biological Sciences Graduate School of Science University of Tokyo Hongo 7-3-1 Bunkyo-ku Tokyo 113-0033 Japan School of Life and Environmental Sciences The University of Sydney Sydney NSW 2006 Australia Institute for Advanced Biosciences Keio University Tsuruoka Yamagata 997-0052 Japan YCI Laboratory for Trans-Omics Young Chief Investigator Program RIKEN Center for Integrative Medical Sciences 1-7-22 Suehiro-cho Tsurumi-ku Yokohama Kanagawa 230-0045 Japan Faculty of Engineering and Information Technologies The University of Sydney Sydney NSW 2006 Australia Department of Pharmacology and Cancer Biology Duke University School of Medicine Duke University Durham NC 27710 USA AMED-CREST AMED 1-7-1 Otemachi Chiyoda-Ku Tokyo 100-0004 Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8562 Japan CREST Japan Science and Technology Agency Bunkyo-ku Tokyo 113-0033 Japan

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13 C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.

关键词： Biological sciences Flux Data Metabolomics Metabolic Flux Analysis

来源：评论

学校读者我要写书评

暂无评论

Visualizing structure and transitions in high-dimensional biological data (vol 54, pg 781, 2019)

引用

NATURE BIOTECHNOLOGY 2020年第1期38卷 108-108页

作者： Moon, Kevin R. van Dijk, David Wang, Zheng Gigante, Scott Burkhardt, Daniel B. Chen, William S. Yim, Kristina van den Elzen, Antonia Hirn, Matthew J. Coifman, Ronald R. Ivanova, Natalia B. Wolf, Guy Krishnaswamy, Smita Department of Mathematics and Statistics Utah State University Logan USA Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven USA Department of Computer Science Yale University New Haven USA School of Basic Medicine Qingdao University Qingdao China Yale Stem Cell Center Department of Genetics Yale University New Haven USA Computational Biology and Bioinformatics Program Yale University New Haven USA Department of Genetics Yale University New Haven USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing USA Department of Mathematics Michigan State University East Lansing USA Applied Mathematics Program Yale University New Haven USA Department of Genetics Center for Molecular Medicine University of Georgia Athens USA Department of Mathematics and Statistics Université de Montréal Montréal Canada Mila—Quebec Artificial Intelligence Institute Montréal Canada

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

引用

Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes (vol 12, pg 631, 2020)

引用

GENETICS IN MEDICINE 2020年第8期22卷 1427-1427页

作者： Chang, Willie H. Mashouri, Pouria Lozano, Alexander X. Johnstone, Brittney Husic, Mia Olry, Annie Maiella, Sylvie Balci, Tugce B. Sawyer, Sarah L. Robinson, Peter N. Rath, Ana Brudno, Michael Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada Department of Computer Science Princeton University Princeton NJ USA Faculty of Medicine University of Toronto Toronto ON Canada Department of Materials Science & Engineering Stanford University Stanford CA USA Sunnybrook Health Sciences Centre Toronto ON Canada Orphanet Institut national de la santé et de la recherche médicale Paris France Medical Genetics Program of Southwestern Ontario London Health Sciences Centre London ON Canada Department of Genetics Children’s Hospital of Eastern Ontario and Children’s Hospital of Eastern Ontario Research Institute University of Ottawa Ottawa ON Canada The Jackson Laboratory for Genomic Medicine Farmington CT USA Institute for Systems Genomics University of Connecticut Farmington CT USA Department of Computer Science University of Toronto Toronto ON Canada Genetics and Genome Biology Program The Hospital for Sick Children Toronto ON Canada University Health Network Toronto ON Canada

Purposecomputational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student *** developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated *** Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research *** enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

关键词： rare diseases phenotype crowdsourcing medical education machine learning

来源：评论

学校读者我要写书评

暂无评论

Global genetic diversity, introgression, and evolutionary adaptation of indicine cattle revealed by whole genome sequencing

引用

NATURE COMMUNICATIONS 2023年第1期14卷 7803页

作者： Chen, Ningbo Xia, Xiaoting Hanif, Quratulain Zhang, Fengwei Dang, Ruihua Huang, Bizhi Lyu, Yang Luo, Xiaoyu Zhang, Hucai Yan, Huixuan Wang, Shikang Wang, Fuwen Chen, Jialei Guan, Xiwen Liu, Yangkai Li, Shuang Jin, Liangliang Wang, Pengfei Sun, Luyang Zhang, Jicai Liu, Jianyong Qu, Kaixing Cao, Yanhong Sun, Junli Liao, Yuying Xiao, Zhengzhong Cai, Ming Mu, Lan Siddiki, Amam Zonaed Asif, Muhammad Mansoor, Shahid Babar, Masroor Ellahi Hussain, Tanveer Silva, Gamamada Liyanage Lalanie Pradeepa Gorkhali, Neena Amatya Terefe, Endashaw Belay, Gurja Tijjani, Abdulfatai Zegeye, Tsadkan Gebre, Mebrate Genet Ma, Yun Wang, Yu Huang, Yongzhen Lan, Xianyong Chen, Hong Migliore, Nicola Rambaldi Colombo, Giulia Semino, Ornella Achilli, Alessandro Sinding, Mikkel-Holger S. Lenstra, Johannes A. Cheng, Haijian Lu, Wenfa Hanotte, Olivier Han, Jianlin Jiang, Yu Lei, Chuzhao Northwest A&F University Key Laboratory of Animal Genetics Breeding and Reproduction of Shaanxi Province College of Animal Science and Technology Yangling China (GRID:grid.144022.1) (ISNI:0000 0004 1760 4150) National Institute for Biotechnology and Genetic Engineering Faisalabad Pakistan (GRID:grid.419397.1) (ISNI:0000 0004 0447 0237) Chinese Academy of Agricultural Sciences (CAAS) CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources Institute of Animal Science Beijing China (GRID:grid.410727.7) (ISNI:0000 0001 0526 1937) Yunnan Academy of Grassland and Animal Science Kunming China (GRID:grid.506866.b) Yunnan University Institute for Ecological Research and Pollution Control of Plateau Lakes School of Ecology and Environment Science Kunming China (GRID:grid.440773.3) (ISNI:0000 0000 9342 2456) Chuxiong Normal University Academy of Science and Technology Chuxiong China (GRID:grid.469523.f) (ISNI:0000 0000 9870 4997) Guangxi Vocational University of Agriculture Nanning China (GRID:grid.469523.f) Guangxi Key Laboratory of Veterinary Biotechnology Guangxi Veterinary Research Institute Nanning China (GRID:grid.418337.a) Guangxi Vocational University of Agriculture Nanning China (GRID:grid.418337.a) Southwest Forestry University College of Landscape and Horticulture Kunming China (GRID:grid.412720.2) (ISNI:0000 0004 1761 2943) Chattogram Veterinary and Animal Sciences University (CVASU) Genomics Research Group Department of Pathology and Parasitology Faculty of Veterinary Medicine Chattogram Bangladesh (GRID:grid.442958.6) National Institute for Biotechnology and Genetic Engineering Faisalabad Pakistan (GRID:grid.419397.1) (ISNI:0000 0004 0447 0237) The University of Agriculture Dera Ismail Khan Pakistan (GRID:grid.412298.4) (ISNI:0000 0000 8577 8102) Virtual University of Pakistan Department of Molecular Biology Islamabad Pakistan (GRID:grid.444943.a) (ISNI:0000 0004 0609 0887) University of Peradeniya Department of Animal Science Peradeniya Sri Lanka (GRID

Indicine cattle, also referred to as zebu (Bos taurus indicus), play a central role in pastoral communities across a wide range of agro-ecosystems, from extremely hot semiarid regions to hot humid tropical regions. However, their adaptive genetic changes following their dispersal into East Asia from the Indian subcontinent have remained poorly documented. Here, we characterize their global genetic diversity using high-quality whole-genome sequencing data from 354 indicine cattle of 57 breeds/populations, including major indicine phylogeographic groups worldwide. We reveal their probable migration into East Asia was along a coastal route rather than inland routes and we detected introgression from other bovine species. Genomic regions carrying morphology-, immune-, and heat-tolerance-related genes underwent divergent selection according to Asian agro-ecologies. We identify distinct sets of loci that contain promising candidate variants for adaptation to hot semi-arid and hot humid tropical ecosystems. Our results indicate that the rapid and successful adaptation of East Asian indicine cattle to hot humid environments was promoted by localized introgression from banteng and/or gaur. Our findings provide insights into the history and environmental adaptation of indicine cattle.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Recurrence quantification analysis of dynamic brain networks

arXiv

引用

arXiv 2020年

作者： Lopes, Marinho A. Zhang, Jiaxiang Krzemiński, Dominik Hamandi, Khalid Chen, Qi Livi, Lorenzo Masuda, Naoki Department of Engineering Mathematics University of Bristol BS8 1UB United Kingdom Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University CardiffCF24 4HQ United Kingdom Center for Studies of Psychological Application School of Psychology South China Normal University Guangzhou510631 China Departments of Computer Science and Mathematics University of Manitoba WinnipegMBR3T 2N2 Canada Department of Computer Science College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4QF United Kingdom Department of Mathematics University at Buffalo State University of New York United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York United States

Evidence suggests that brain network dynamics is a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in brain function in health and other neurological disorders. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Correction: Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers

引用

BMC Digital Health 2023年第1期1卷 1-1页

作者： Thomas Wetere Tulu Ching Long Chan Chun Hei Wu Kei Hang Katie Chan Tsz Kin Wan Peter Yat Ming Woo Cee Zhung Steven Tseng Asmir Vodencarevic Cristina Menni Department of Biomedical Sciences City University of Hong Kong Hong Kong SAR China Computational Data Science Program Addis Ababa University Addis Ababa Ethiopia Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China Department of Epidemiology and Center for Global Cardiometabolic Health School of Public Health Brown University Providence RI USA Department of Neurosurgery Kwong Wah Hospital Hong Kong SAR China Department of Medicine and Geriatrics Kwong Wah Hospital Hong Kong SAR China Innovative Medicines Novartis Pharma GmbH 90429 Nuremberg Germany Department of Twin Research King’s College London London UK

来源：评论

学校读者我要写书评

暂无评论

Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

引用

SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

来源：评论

学校读者我要写书评

暂无评论

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

Research Square

引用

Research Square 2021年

作者： Kuchroo, Manik Huang, Jessie Wong, Patrick Grenier, Jean-Christophe Shung, Dennis Tong, Alexander Lucas, Carolina Klein, Jon Burkhardt, Daniel B. Gigante, Scott Godavarthi, Abhinav Rieck, Bastian Israelow, Benjamin Simonov, Michael Mao, Tianyang Oh, Ji Eun Silva, Julio Takahashi, Takehiro Odio, Camila D. Casanovas-Massana, Arnau Fournier, John Farhadian, Shelli Dela Cruz, Charles S. Ko, Albert I. Hirn, Matthew J. Wilson, F. Perry Hussin, Julie Wolf, Guy Iwasaki, Akiko Krishnaswamy, Smita Department of Neuroscience Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Immunobiology Yale University New HavenCT United States Montreal Heart Institute MontréalQC Canada Department of Medicine Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Computational Biology Bioinformatics Program Yale University New HavenCT United States Department of Applied Mathematics Yale University New HavenCT United States Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Epidemiology of Microbial Diseases Yale School of Public Health New HavenCT United States Department of Medicine Section of Infectious Diseases Yale University School of Medicine New HavenCT United States Department of Medicine Section of Pulmonary and Critical Care Medicine Yale University School of Medicine New HavenCT United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Michigan State University East LansingMI United States Clinical and Translational Research Accelerator Department of Medicine Yale University New HavenCT United States Faculty of Medicine Université de Montréal Québec Canada Mila – Quebec AI institute MontréalQC Canada Department of Mathematics and Statistics Université de Montréal MontréalQC Canada Howard Hughes Medical Institute Chevy ChaseMD United States

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hiCD66blo neutrophil and IFNγ+GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome. © 2021, CC BY.

关键词： Coronavirus

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：