检索结果-内蒙古大学图书馆

Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

引用

Nature genetics 2018年第5期50卷 766-767页

作者： Valérie Turcot Yingchang Lu Heather M Highland Claudia Schurmann Anne E Justice Rebecca S Fine Jonathan P Bradfield Tõnu Esko Ayush Giri Mariaelisa Graff Xiuqing Guo Audrey E Hendricks Tugce Karaderi Adelheid Lempradl Adam E Locke Anubha Mahajan Eirini Marouli Suthesh Sivapalaratnam Kristin L Young Tamuno Alfred Mary F Feitosa Nicholas G D Masca Alisa K Manning Carolina Medina-Gomez Poorva Mudgal Maggie C Y Ng Alex P Reiner Sailaja Vedantam Sara M Willems Thomas W Winkler Gonçalo Abecasis Katja K Aben Dewan S Alam Sameer E Alharthi Matthew Allison Philippe Amouyel Folkert W Asselbergs Paul L Auer Beverley Balkau Lia E Bang Inês Barroso Lisa Bastarache Marianne Benn Sven Bergmann Lawrence F Bielak Matthias Blüher Michael Boehnke Heiner Boeing Eric Boerwinkle Carsten A Böger Jette Bork-Jensen Michiel L Bots Erwin P Bottinger Donald W Bowden Ivan Brandslund Gerome Breen Murray H Brilliant Linda Broer Marco Brumat Amber A Burt Adam S Butterworth Peter T Campbell Stefania Cappellani David J Carey Eulalia Catamo Mark J Caulfield John C Chambers Daniel I Chasman Yii-Der I Chen Rajiv Chowdhury Cramer Christensen Audrey Y Chu Massimiliano Cocca Francis S Collins James P Cook Janie Corley Jordi Corominas Galbany Amanda J Cox David S Crosslin Gabriel Cuellar-Partida Angela D'Eustacchio John Danesh Gail Davies Paul I W Bakker Mark C H Groot Renée Mutsert Ian J Deary George Dedoussis Ellen W Demerath Martin Heijer Anneke I Hollander Hester M Ruijter Joe G Dennis Josh C Denny Emanuele Di Angelantonio Fotios Drenos Mengmeng Du Marie-Pierre Dubé Alison M Dunning Douglas F Easton Todd L Edwards David Ellinghaus Patrick T Ellinor Paul Elliott Evangelos Evangelou Aliki-Eleni Farmaki I Sadaf Farooqi Jessica D Faul Sascha Fauser Shuang Feng Ele Ferrannini Jean Ferrieres Jose C Florez Ian Ford Myriam Fornage Oscar H Franco Andre Franke Paul W Franks Nele Friedrich Ruth Frikke-Schmidt Tessel E Galesloot Wei Gan Ilaria Gandin Paolo Gasparini Jane Gibson Vilmantas Giedraitis Anette P Gjesing Penny Gordon-Larsen Mathias Gorski Hans-Jörgen Montreal Heart Institute Université de Montréal Montreal Quebec Canada. Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University School of Medicine Nashville TN USA. Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York NY USA. Genetics of Obesity and Related Metabolic Traits Program Icahn School of Medicine at Mount Sinai New York NY USA. Department of Epidemiology University of North Carolina Chapel Hill NC USA. Human Genetics Center University of Texas School of Public Health University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences University of Texas Health Science Center at Houston Houston TX USA. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genetics Harvard Medical School Boston MA USA. Division of Endocrinology and Center for Basic and Translational Obesity Research Boston Children's Hospital Boston MA USA. Center for Applied Genomics Division of Human Genetics Children's Hospital of Philadelphia Philadelphia PA USA. Quantinuum Research LLC San Diego CA USA. Estonian Genome Center University of Tartu Tartu Estonia. Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Nashville TN USA. Institute for Translational Genomics and Population Sciences LABioMed at Harbor-UCLA Medical Center Torrance CA USA. Wellcome Trust Sanger Institute Hinxton UK. Department of Mathematical and Statistical Sciences University of Colorado Denver CO USA. Wellcome Trust Centre for Human Genetics University of Oxford Oxford UK. Department of Biological Sciences Faculty of Arts and Sciences Eastern Mediterranean University Famagusta Cyprus. Max Planck Institute of Immunobiology and Epigenetics Freiburg Germany. Department of Biostatistics and Center for Statistical Genetics University of M

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Variant interpretation using population databases: lessons from gnomAD

arXiv

引用

arXiv 2021年

作者： Gudmundsson, Sanna Singer-Berk, Moriel Watts, Nicholas A. Phu, William Goodrich, Julia K. Solomonson, Matthew Rehm, Heidi L. MacArthur, Daniel G. O'Donnell-Luria, Anne Abreu, Maria Aguilar Salinas, Carlos A. Ahmad, Tariq Albert, Christine M. Alföldi, Jessica Ardissino, Diego Armean, Irina M. Atzmon, Gil Banks, Eric Barnard, John Baxter, Samantha M. Beaugerie, Laurent Benjamin, Emelia J. Benjamin, David Bergelson, Louis Boehnke, Michael Bonnycastle, Lori L. Bottinger, Erwin P. Bowden, Donald W. Bown, Matthew J. Brant, Steven Calvo, Sarah E. Campos, Hannia Chambers, John C. Chan, Juliana C. Chao, Katherine R. Chapman, Sinéad Chasman, Daniel Chisholm, Rex Cho, Judy Chowdhury, Rajiv Chung, Mina K. Chung, Wendy Cibulskis, Kristian Cohen, Bruce Collins, Ryan L. Connolly, Kristen M. Correa, Adolfo Covarrubias, Miguel Cummings, Beryl Dabelea, Dana Daly, Mark J. Danesh, John Darbar, Dawood Denny, Joshua Donnelly, Stacey Duggirala, Ravindranath Dupuis, Josée Ellinor, Patrick T. Elosua, Roberto Emery, James England, Eleina Erdmann, Jeanette Esko, Tõnu Evangelista, Emily Farjoun, Yossi Fatkin, Diane Ferriera, Steven Florez, Jose Francioli, Laurent Franke, Andre Färkkilä, Martti Gabriel, Stacey Garimella, Kiran Gauthier, Laura D. Gentry, Jeff Getz, Gad Glahn, David C. Glaser, Benjamin Glatt, Stephen J. Goldstein, David Gonzalez, Clicerio Goodrich, Julia Groop, Leif Gudmundsson, Sanna Gupta, Namrata Haessly, Andrea Haiman, Christopher Hall, Ira Hanis, Craig Harms, Matthew Hiltunen, Mikko Holi, Matti M. Hultman, Christina M. Jalas, Chaim Jeandet, Thibault Kallela, Mikko Kaplan, Diane Kaprio, Jaakko Karczewski, Konrad J. Kathiresan, Sekar Kenny, Eimear Kim, Bong-Jo Kim, Young Jin Kirov, George Kooner, Jaspal Koskinen, Seppo Krumholz, Harlan M. Kugathasan, Subra Kwak, Soo Heon Laakso, Markku Lake, Nicole Langsford, Trevyn Laricchia, Kristen M. Lehtimäki, Terho Lek, Monkol Lipscomb, Emily Llanwarne, Christopher Loos, Ruth J.F. Lubitz, Steven A. Luna, Teresa Tusie Ma, Ronald C.W. MacArthur, Daniel G. Marcus, Gregory M. Marrugat, Ja University of Miami Miller School of Medicine Gastroenterology Miami United States Unidad de Investigacion de Enfermedades Metabolicas Instituto Nacional de Ciencias Medicas y Nutricion Mexico City Mexico Peninsula College of Medicine and Dentistry Exeter United Kingdom Division of Preventive Medicine Brigham and Women's Hospital BostonMA United States Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Analytic and Translational Genetics Unit Massachusetts General Hospital BostonMA United States Department of Cardiology University Hospital Parma Italy European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton Cambridge United Kingdom Department of Biology Faculty of Natural Sciences University of Haifa Haifa Israel Department of Medicine and Genetics Albert Einstein College of Medicine BronxNY United States Data Science Platform Broad Institute of MIT and Harvard CambridgeMA United States Department of Quantitative Health Sciences Lerner Research Institute Cleveland Clinic ClevelandOH United States Sorbonne Université APHP Gastroenterology Department Saint Antoine Hospital Paris France NHLBI Boston University's Framingham Heart Study FraminghamMA United States Department of Medicine Boston University School of Medicine BostonMA United States Department of Epidemiology Boston University School of Public Health BostonMA United States Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann ArborMS United States National Human Genome Research Institute National Institutes of Health BethesdaMD United States The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Biochemistry Wake Forest School of Medicine Winston-SalemNC

Reference population databases are an essential tool in variant and gene interpretation. Their use guides the identification of pathogenic variants amidst the sea of benign variation present in every human genome, and supports the discovery of new disease-gene relationships. The Genome Aggregation database (gnomAD) is currently the largest and most widely used publicly available collection of population variation from harmonized sequencing data. The data is available through the online gnomAD browser (https://***/) that enables rapid and intuitive variant analysis. This review provides guidance on the content of the gnomAD browser, and its usage for variant and gene interpretation. We introduce key features including allele frequency, per-base expression levels, constraint scores, and variant co-occurrence, alongside guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease. © 2021, CC BY.

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence for Predicting Recurrence of Clear Cell Renal Cell Carcinoma Using Multimodal data: A Multicenter Study

SSRN

引用

SSRN 2024年

作者： Zang, Xinyi Xia, Yujia Xiao, Haibing Luo, Haolun Si, Guiming Hou, Naiqiao Haoni, Axide Chen, Tianyi Pu, Xinyuan Zi, Xiangyu Xu, Lijun Zhu, Jin Xu, Zhipeng Wang, Jianning Wang, Zaoyu Xia, Jun Cao, Dengfeng Yin, Yu Wu, Xiaorong Kong, Wen Huang, Jiwei Zhang, Jin Chen, Yonghui Huang, Yiran Xue, Wei Wang, Keliang Liang, Chaozhao Zheng, Junhua Yu, Zhangsheng Zhai, Wei Department of Urology Renji Hospital School of Medicine in Shanghai Jiao Tong University 160 Pujian Road Shanghai200127 China Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China SJTU-Yale Joint Center for Biostatistics and Data Science School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Department of Urology The First Affiliated Hospital of Anhui Medical University Anhui Medical University Anhui Hefei China Department of Urology Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Urology The Fourth Affiliated Hospital of Harbin Medical University Heilongjiang Key Laboratory of Scientific Research in Urology Harbin150001 China Department of Urology The Second Affiliated Hospital of Soochow University Jiangsu Suzhou215004 China Department of Urology The First Affiliated Hospital Shandong First Medical University Jinan China Department of Pathology Renji Hospital School of Medicine in Shanghai Jiao Tong University 160 Pujian Road Shanghai200127 China Department of Pathology The First Affiliated Hospital of Anhui Medical University Anhui Hefei China Clinical Research Institute Shanghai Jiao Tong University School of Medicine Shanghai China Shanghai Immune Therapy Institute State Key Laboratory of Oncogenes and Related Genes Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200127 China Department of Evidence-Based Medicine China

Background: Current prognostic models have limitations in predicting postoperative recurrence in renal clear cell carcinoma due to tumour heterogeneity. This study aims to develop and validate a multimodal predictive recurrence score (MPRS) to enhance recurrence prediction and patient stratification by integrating clinical features, CT imaging, and histological whole section images (WSIs). Methods: This multicenter retrospective study analyzed multimodal data from 1,145 ccRCC patients treated surgically across 6 centers in China and WSIs from 503 TCGA patients. The primary outcome was disease-free survival (DFS), defined as the time from surgery to the first recurrence or end of follow-up. Assessment of model efficacy using time-dependent ROC curves, calibration curves, survival curves. Assessing the impact of regions on the model using Grad-CAM visualisation analysis. Findings: MPRS performed better than other unimodal models in time-dependent ROC curves. At 3 years survival, the AUC values for MPRS were 0.944, 0.907, and 0.835;at 5 years survival, the AUC values of MPRS were 0.960, 0.897 and 0.829 for the training, validation, and test sets, respectively. There was strong agreement between MPRS predictions and observations in the calibration curves. The MPRS showed significant differences in subgroup analyses for both predicting DFS and various prognostic models. Interpretation: The MPRS is effective in predicting recurrence risk by integrating multimodal data and provides insights into the management of patients with ccRCC, potentially guiding adjuvant therapeutic decisions. Funding: This study was supported by National Natural science Foundation of China (82173214, 82473386);the National Key research and Development Program of China (2022YFC2505305);Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute;Shanghai science and Technology Commission Medical Innovation Clinical research Special Project (23Y21900400);Excellent Project o

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Methods for Evaluating Medical Tests and Biomarkers

引用

Diagnostic and Prognostic research 2017年第1期1卷 1-34页

作者： Gowri Gopalakrishna Miranda Langendam Patrick Bossuyt Mariska Leeflang Rob Scholten Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Marie Westwood Jos Kleinen Richard Riley Gary Collins Hans Reitsma Karel Moons Antonia Zapf Katharina Kramer Annika Hoyer Oliver Kuss J. Ensor R. D. Riley J. J. Deeks E. C. Martin Gerta Rücker Martin Schumacher Susanne Steinhauser Joie Ensor Kym Snell Brian Willis Jon Deeks Thomas Debray Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A. Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Sabine Van Huffel Jan Verbakel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O’Sullivan Rafael Perera Brian Nicholson Hannah L. Bromley Tracy E. Roberts Adele Francis Denniis Petrie G. Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G. Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Peter Selby Doug Altman William Rosenberg Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Johannes Reitsma Anne Rutjes Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Bavo De Cock Daan Nieboer Ewout W. Steyerberg Micael J. Pencina Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Zoe Plummer Barnaby Reeves Chris Rogers Andrew Mumford Kurtis Lee Veerle Verheyden Gianni D. Angelini Gavin J. Murphy Jeremy Huddy Melody Ni George Hanna Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands Department of Clinical Epidemiology and Biostatistics Academic Medical Center University of Amsterdam Amsterdam Netherlands Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK EPPI-Centre Department of Social Science University College London London UK Division of Health and Social Care Research King’s College London London UK College of Computer and Information Science Northeastern University Boston USA University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK Institute of Applied Health Research University of Birmingham Birmingham UK School of Social and Community Medicine University of Bristol Bristol UK Kleijnen Systematic Reviews Ltd York UK Research Institute for Primary Care and Health Sciences Keele University Keele UK Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Cochrane Netherlands University Medical Center Utrecht Utrecht The Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Institute for Biometry and Epidemiology German Diabetes Center Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf Germany Manchester Pharmacy School University of Manchester Manchester UK Institute for Medical Biometry and Statistics Faculty of Medicine and Medical Center – University of Freiburg Stefan-Meier-Str. 26 79104 Freiburg Germany

来源：评论

学校读者我要写书评

暂无评论

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

引用

Journal of proteome research 2023年第4期22卷 1024-1042页

作者： Gilbert S Omenn Lydie Lane Christopher M Overall Charles Pineau Nicolle H Packer Ileana M Cristea Cecilia Lindskog Susan T Weintraub Sandra Orchard Michael H A Roehrl Edouard Nice Siqi Liu Nuno Bandeira Yu-Ju Chen Tiannan Guo Ruedi Aebersold Robert L Moritz Eric W Deutsch University of Michigan Ann Arbor Michigan48109 United States. Institute for Systems Biology Seattle Washington98109 United States. CALIPHO Group SIB Swiss Institute of Bioinformatics and University of Geneva 1015Lausanne Switzerland. University of British Columbia Vancouver British ColumbiaV6T 1Z4 Canada. French Institute of Health and Medical Research 35042RENNESCedexFrance. Macquarie University Sydney New South Wales2109 Australia. Griffith University's Institute for Glycomics Sydney New South Wales2109 Australia. Princeton University Princeton New Jersey08544 United States. Uppsala University 752 36Uppsala Sweden. University of Texas Health Science Center-San Antonio San Antonio Texas78229-3900 United States. EMBL-EBI Hinxton CambridgeshireCB10 1SD United Kingdom. Memorial Sloan Kettering Cancer Center New York New York10065 United States. Monash University ClaytonVictoria3800 Australia. BGI Group Shenzhen518083 P. R. China. University of California San Diego La Jolla California92093 United States. National Taiwan University Academia Sinica Nankang Taipei11529 Taiwan. Westlake University Guomics Laboratory of Big Proteomic Data Hangzhou310024 ZhejiangProvinceP. R. China. Institute of Molecular Systems Biology in ETH Zurich 8092Zurich Switzerland.

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

关键词： Biology and Disease-HPP (B/D-HPP) Grand Challenge Project Human Protein Atlas Human Proteome Project (HPP) Mass Spectrometry Interactive Virtual Environment (MassIVE) PeptideAtlas Ribo-Seq chromosome-centric HPP (C-HPP) missing proteins (MP) neXtProt protein existence (PE metrics) non-MS PE1 proteins small open reading frames (smORFs) uncharacterized protein existence 1 (uPE1)

来源：评论

学校读者我要写书评

暂无评论

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

arXiv

引用

arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： data mining

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

Improved Risk Stratification of Smoldering Multiple Myeloma (SMM) Using Trajectory data in the Pangea 2.0 Model: A Multicenter Study in 1,431 Participants

引用

Blood 2024年 144卷 1017-1017页

作者： Floris Chabrun Daniel Schwartz Susanna Gentile Tulika R Gupta Noelia Collado Gisbert Esperanza Martín-Sánchez Rosalinda Termini Jacqueline F Perry Annie Cowan Federico Ferrari Samuel Freeman Robert A. Redd Vidhi Patel Patrick Costello Catherine Tobia Romanos Sklavenitis-Pistofidis Habib El-Khoury Michael Timonian David J Lee Elizabeth D. Lightbody Irene Ghobrial Dana-Farber Cancer Institute Boston MA Harvard T.H. Chan School of Public Health Boston MA Cancer Center Clinica Universidad de Navarra (CCUN) Centro de Investigacion Medica Aplicada (CIMA) Instituto de Investigacion Sanitaria de Navarra (IdiSNA) CIBER-ONC numbers CB16/12/00369 and CB16/12/00489 Pamplona Spain Biostatistics and Research Decision Sciences Merck & Co Rahway NJ Bioinformatics Program Broad Institute of MIT and Harvard Cambridge MA Department of Data Science Dana-Farber Cancer Institute Boston MA Broad Institute of MIT and Harvard Cambridge MA University of Chicago Medical Center Chicago IL Harvard Medical School Boston MA

Background The 20/2/20 model is the current gold standard to stratify smoldering multiple myeloma (SMM) patients at baseline into three subgroups (low, intermediate, and high) according to the risk of progression based on the free light chain ratio (FLCr), M-protein concentration, and percentage of bone marrow (BM) plasma cells (PC). Evolving patterns that may alter the risk of progression are not considered in this static model. We previously proposed the PANGEA model that allows for personalized risk prediction using FLCr, M-protein, creatinine, age, hemoglobin trajectory, and optionally BM PC. We developed an improved PANGEA 2.0 model that includes trajectory modeling of these biomarkers to capture evolving patterns and improve predictions of MM progression. Methods We conducted a retrospective review of clinical data from 1,431 participants diagnosed with SMM at 4 international sites (Dana-Farber Cancer Institute, Boston, US, n = 737; National and Kapodistrian University of Athens, Greece, n = 379; University College London, UK, n = 97; and University of Navarra, Spain, n = 218). Dana-Farber participants comprised a training cohort to identify biomarker trajectories and to develop the PANGEA 2.0 model. The model was validated on two international cohorts: validation cohort 1 included patients from Greece and the UK (n = 476) and validation cohort 2 included patients from Spain (n = 218). The longitudinal data collected from 2018-2024 included current values and historical trajectories of age, M-protein, FLCr, creatinine, and hemoglobin, as well as BM PC (optional). We used a systematic grid search with 5-fold cross-validation to determine optimal trajectory definitions for M-protein, FLCr, creatinine, and hemoglobin. For each, we evaluated seven binary trajectory definitions based on average increase over time (slopes) or recent increase from the previous visit on absolute or relative (%) increase scales, with varying thresholds and time periods. We used Cox reg

关键词：

来源：评论

学校读者我要写书评

暂无评论

Integrative metabolomics-genomics approach reveals that pathways related to the metabolism of acylcarnitines and amines are new potential targets of Alzheimer’s disease

引用

Alzheimer's & Dementia 2020年第S2期16卷

作者： Emrin Horgusluoglu-Moloch Minghui Wang Matthias Arnold Jan Krumsiek Beatriz Galindo-Prieto Gabi Kastenmüller Xianlin Han Rebecca Baillie Kwangsik Nho Andrew J. Saykin Rima F. Kaddurah-Daouk Bin Zhang Icahn Institute of Genomics and Multiscale Biology New York NY USA Icahn School of Medicine at Mount Sinai New York NY USA Institute of Bioinformatics and Systems Biology Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Helmholtz Zentrum Munchen Neuherberg Germany Sanford-Burnham Medical Research Institute Orlando FL USA Rosa & Co LLC San Carlos CA USA Indiana University School of Medicine Indianapolis IN USA Department of Psychiatry and Behavioral Sciences Duke University Durham NC USA Icahn Institute for Data Science and Genomic Technology New York NY USA

Background The metabolic basis of Alzheimer’s Disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis remain elusive. In this study, we systematically interrogated metabolomics, genetics, proteomics, and clinical data from the matched subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to identify key drivers and metabolic pathways associated with changes in metabolites with disease severity. Method 140 Metabolites in fasting serum samples from the ADNI (362 controls, 94 significant memory concerns, 764 mild cognitive impairment, and 298 AD) were analyzed using the AbsoluteIDQ-p180 kit. Values adjusted for age, gender, body mass index (BMI), education, cohort, and medication. A metabolite co-expression network was constructed using Multiscale Embedded Gene co-Expression Network Analysis (MEGENA). Co-expressed metabolite modules were then prioritized for the strength of association with clinical/cognitive and pathology biomarker variables. Correlation analysis of the co-metabolite modules and the matched gene/protein expression data was performed. The ROS/MAP cohort was used as a replication study of the co-expression network. Six brain transcriptomic datasets from the Mount Sinai Brain Bank, ROS/MAP, and MAYO clinic cohorts were utilized to build up gene-centered correlation networks to elucidate functions of upstream regulators of candidate metabolites. Result Modules comprised of short-chain acylcarnitines/amino acids (M8, M3), and medium/long-chain acylcarnitines (M6) were most associated with worse AD clinical outcomes, including episodic memory scores and disease severity (CDR-SB) (Figure 1A-C). CPT1A gene expression was highly correlated with an increased level of the medium/long-chain acylcarnitines (corrected p-value = 1.87 × 10-6). Increased ABCA1 gene expression and adiponectin protein (a regulator of ABCA1) levels corresponded to decreased short-chain acylcarnitines and amine

关键词：

来源：评论

学校读者我要写书评

暂无评论

Publisher Correction: Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set

引用

Nature communications 2019年第1期10卷 1098页

作者： N Del-Toro M Duesbury M Koch L Perfetto A Shrivastava D Ochoa O Wagih J Piñero M Kotlyar C Pastrello P Beltrao L I Furlong I Jurisica H Hermjakob S Orchard P Porras European Bioinformatics Institute (EMBL-EBI) European Molecular Biology Laboratory Wellcome Genome Campus Hinxton CB10 1SD UK. Novartis Institutes for BioMedical Research (NIBR) Maulbeerstrasse 66 4058 Basel Canton of Basel-Stadt Switzerland. Deep Genomics MaRS Centre 661 University Ave Suite 480 Toronto ON M5G 1M1 Canada. Research Programme on Biomedical Informatics (GRIB) Department of Experimental and Health Sciences (DCEXS) Hospital del Mar Medical Research Institute (IMIM) Universitat Pompeu Fabra (UPF) 08003 Barcelona Spain. Krembil Research Institute Data Science Discovery Centre for Chronic Diseases University Health Network 5KD-407 60 Leonard Avenue Toronto ON M5T 0S8 Canada. Departments of Medical Biophysics and Computer Science University of Toronto Toronto M4B 1B5 Canada. State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences Beijing Institute of Life Omics 102206 Beijing China. European Bioinformatics Institute (EMBL-EBI) European Molecular Biology Laboratory Wellcome Genome Campus Hinxton CB10 1SD UK. pporras@ebi.ac.uk.

In the original HTML version of this Article, the order of authors within the author list was incorrect. The IMEx Consortium contributing authors were incorrectly listed as the last author and should have been listed as the first author. This error has been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.

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：