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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Multi-omic Microbiome Profiles in the Female Reproductive Tract in Early Pregnancy

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Infectious Microbes & Diseases 2019年第2期1卷 49-60页

作者： Sophonie Jean Bernice Huang Hardik I.Parikh David J.Edwards J.Paul Brooks Naren Gajenthra Kumar Nihar U.Sheth Vishal Koparde Ekaterina Smirnova Snehalata Huzurbazar Philippe H.Girerd Dayanjan S.Wijesinghe Jerome F.StraussIII Myrna G.Serrano Jennifer M.Fettweis Kimberly K.Jefferson Gregory A.Buck Department of Microbiology and Immunology Virginia Commonwealth UniversityRichmondVA Center for Microbiome Engineering and Data Analysis Virginia Commonwealth UniversityRichmondVA Department of Statistical Sciences and Operations Research Virginia Commonwealth UniversityRichmondVA Supply Chain Management and Analytics School of BusinessVirginia Commonwealth UniversityRichmondVA Life Sciences Virginia Commonwealth UniversityRichmondVA DNAnexus Inc. Mountain ViewCA Frederick National Laboratory for Cancer Research FrederickMD Department of Biostatistics Virginia Commonwealth UniversityRichmondVA Department of Statistics University of WyomingLaramieWY Obstetrics and Gynecology Virginia Commonwealth UniversityRichmondVA Pharmacotherapy and Outcomes Science Virginia Commonwealth UniversityRichmondVA Department of Computer Science Virginia Commonwealth UniversityRichmondVA.

The vaginal microbiome likely influences host signaling compounds within the reproductive tract,including pro-inflammatory signals,which may play an important role during *** lactobacilli are associated with positive pregnancy outcome,whereas bacterial vaginosis,a dysbiosis of the vaginal microbiome,is associated with an increased risk of adverse pregnancy outcomes including preterm *** the host response could be predicted based on the taxonomic composition of the vaginal microbiome,particularly early in pregnancy,then those predictions could potentially be used to personalize intervention methods to reduce preterm birth and other adverse *** this proof of principle study,we apply multivariate strategies to analyze 16S rRNA-based taxonomic surveys in conjunction with targeted immuno-proteomic and lipidomic data from vaginal samples from 58 women enrolled in the Multi-Omic Microbiome Study-Pregnancy Initiative during early *** between the vaginal microbiome and the vaginal lipidome have not been previously *** from this study reveal significant multiple pairwise associations between microbial taxa,specific eicosanoids and sphingomyelins,and *** the biologic significance of these associations is not yet known,these results support the utility of such multi-omic approaches as a means to predict the impact of the microbiome on the host.

关键词： vaginal microbiome preterm birth multi-omics metagenomics cytokine profiles lipidomics

Author Correction: Predicting the efficiency of prime editing guide RNAs in human cells

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Nature biotechnology 2024年第3期42卷 529页

作者： Hui Kwon Kim Goosang Yu Jinman Park Seonwoo Min Sungtae Lee Sungroh Yoon Hyongbum Henry Kim Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. Electrical and Computer Engineering Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Graduate School of Data Science Seoul National University Seoul Republic of Korea. Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac. Severance Biomedical Science Institute Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of NanoScience and Technology Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac.

Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin

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Research Square

Research Square 2021年

作者： Wang, Guan Kitaoka, Traci Crawford, Ali Mao, Qian Hesketh, Andrew Guppy, Fergus M. Ash, Garrett I. Liu, Jason Gerstein, Mark B. Pitsiladis, Yannis P. Sport and Exercise Science and Sports Medicine Research and Enterprise Group University of Brighton Brighton United Kingdom Centre for Regenerative Medicine and Devices University of Brighton Brighton United Kingdom Illumina San DiegoCA United States BGI Shenzhen China School of Pharmacy and Biomolecular Sciences University of Brighton Brighton United Kingdom Centre for Stress and Age-related Disease University of Brighton Brighton United Kingdom Veterans Affairs Connecticut Healthcare System West HavenCT United States Center for Medical Informatics Yale University New HavenCT United States Program in Computational Biology & Bioinformatics Yale University New HavenCT United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States

RNA-seq has matured and become an important tool for studying RNA biology. Here we compared two RNA-seq (Illumina sequencing by synthesis and MGI DNBSEQTM) and two microarray platforms (Illumina Expression BeadChip and GeneChipTM Human Transcriptome Array 2.0) in healthy individuals administered recombinant human erythropoietin for transcriptome-wide quantification of differential gene expression. The results show that total RNA sequencing combined with DNB-seq produced a multitude of genes of biological relevance and significance in response to recombinant human erythropoietin, in contrast to other platforms. Through data triangulation linking genes to functions, genes representing the processes of erythropoiesis as well as non-erythropoietic functions of erythropoietin were unveiled. This study provides a knowledge base of genes characterising the responses to recombinant human erythropoietin through cross-platform comparison and validation. © 2021, CC BY.

关键词： RNA

Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000-17: analysis for the Global Burden of Disease Study 2017 (vol 395, pg 1779, 2020)

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LANCET 2020年第10239期395卷 1762-1762页

作者： Reiner, R. C., Jr. Hay, S., I Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Global Health School of Medicine University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States College of Health and Medical Sciences Haramaya University Harar Ethiopia Department of Epidemiology and Biostatistics Haramaya University Harar Ethiopia Department of Medical Laboratory Sciences Haramaya University Harar Ethiopia School of Nursing and Midwifery Haramaya University Harar Ethiopia School of Pharmacy Haramaya University Harar Ethiopia School of Public Health Haramaya University Harar Ethiopia Haramaya University Harar Ethiopia Advanced Diagnostic and Interventional Radiology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Biology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Research Institute Tehran University of Medical Sciences Tehran Iran Department of Economics and Management Sciences for Health Tehran University of Medical Sciences Tehran Iran Department of Environmental Health Engineering Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Department of Microbiology Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Hematology-Oncology and Stem Cell Transplantation Research Center Tehran University of Medical Sciences Tehran Iran Iran National Institute of Health Research Tehran University of Medical Sciences Tehran Iran Metabolomics and

Summary Background Across low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood *** We used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor *** The greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1-65·8), 17·4% (7·7-28·4), and 59·5% (34·2-86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy *** By co-analysing geospatial trends in d

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Classification of datasets with imputed missing values: does imputation quality matter?

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arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

A pulsar-like swing in the polarisation position angle of a nearby fast radio burst

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arXiv 2024年

作者： McKinven, Ryan Bhardwaj, Mohit Eftekhari, Tarraneh Kilpatrick, Charles D. Kirichenko, Aida Pal, Arpan Cook, Amanda M. Gaensler, B.M. Giri, Utkarsh Kaspi, Victoria M. Michilli, Daniele Nimmo, Kenzie Pearlman, Aaron B. Pleunis, Ziggy Sand, Ketan R. Stairs, Ingrid Andersen, Bridget C. Andrew, Shion Bandura, Kevin Brar, Charanjot Cassanelli, Tomas Chatterjee, Shami Curtin, Alice P. Dong, Fengqiu Adam Eadie, Gwendolyn Fonseca, Emmanuel Ibik, Adaeze L. Kaczmarek, Jane F. Kharel, Bikash Lazda, Mattias Leung, Calvin Li, Dongzi Main, Robert Masui, Kiyoshi W. Mena-Parra, Juan Ng, Cherry Pandhi, Ayush Patil, Swarali Shivraj Prochaska, J. Xavier Rafiei-Ravandi, Masoud Scholz, Paul Shah, Vishwangi Shin, Kaitlyn Smith, Kendrick Department of Physics McGill University 3600 rue University MontréalQCH3A 2T8 Canada Trottier Space Institute McGill University 3550 rue University MontréalQCH3A 2A7 Canada McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA15213 United States Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada David A. Dunlap Department of Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Division of Physical and Biological Sciences University of California Santa Cruz Santa CruzCA95064 United States Department of Physics University of Wisconsin-Madison 1150 University Ave MadisonWI53706 United States Instituto de Astronomía Universidad Nacional Autónoma de México Apdo. Postal 877 Baja California Ensenada22800 Mexico MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States National Centre for Radio Astrophysics Tata Institute of Fundamental Research Pune411007 India Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Canada Lane Department of Computer Science and Electrical Engineering 1220 Evansdale Drive PO Box 6109 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8370451 Chile Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Department of Statistical Sciences University of Toronto 700 University Av

Fast radio bursts (FRBs) last for milliseconds and arrive at Earth from cosmological distances. While their origin(s) and emission mechanism(s) are presently unknown, their signals bear similarities with the much less luminous radio emission generated by pulsars within our Galaxy1 and several lines of evidence point toward neutron star origins2,3. For pulsars, the linear polarisation position angle (PA) often exhibits evolution over the pulse phase that is interpreted within a geometric framework known as the rotating vector model (RVM)4. Here, we report on a fast radio burst, FRB 20221022A, detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and localized to a nearby host galaxy (∼ 65 Mpc), MCG+14-02-011. This one-off FRB displays a ∼ 130 degree rotation of its PA over its ∼ 2.5 ms burst duration, closely resembling the "S"-shaped PA evolution commonly seen from pulsars and some radio magnetars. The PA evolution disfavours emission models involving shocks far from the source5–7 and instead suggests magnetospheric origins8–10 for this source which places the emission region close to the FRB central engine, echoing similar conclusions drawn from tempo-polarimetric studies of some repeating sources11,12. This FRB’s PA evolution is remarkably well-described by the RVM and, although we cannot determine the inclination and magnetic obliquity due to the unknown period/duty cycle of the source, we can dismiss extremely short-period pulsars (e.g., recycled millisecond pulsars) as potential progenitors. RVM-fitting appears to favour a source occupying a unique position in the period/duty cycle phase space that implies tight opening angles for the beamed emission, significantly reducing burst energy requirements of the source. © 2024, CC BY.

关键词： Pulsars

Beyond the exome: what’s next in diagnostic testing for Mendelian conditions

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arXiv 2023年

作者： Wojcik, Monica H. Reuter, Chloe M. Marwaha, Shruti Mahmoud, Medhat Duyzend, Michael H. Barseghyan, Hayk Yuan, Bo Boone, Philip M. Groopman, Emily E. Délot, Emmanuèle C. Jain, Deepti Sanchis-Juan, Alba Starita, Lea M. Talkowski, Michael Montgomery, Stephen B. Bamshad, Michael J. Chong, Jessica X. Wheeler, Matthew T. Berger, Seth I. O'Donnell-Luria, Anne Sedlazeck, Fritz J. Miller, Danny E. Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA02142 United States Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School BostonMA02115 United States Division of Newborn Medicine Boston Children's Hospital Harvard Medical School BostonMA United States Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA94305 United States Human Genome Sequencing Center Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Center for Genomic Medicine Massachusetts General Hospital BostonMA02114 United States Center for Genetics Medicine Research Children's National Research Institute Children's National Hospital WashingtonDC20010 United States Department of Genomics and Precision Medicine School of Medicine and Health Sciences George Washington University WashingtonDC20037 United States Department of Molecular and Human Genetics Human Genome Sequencing Center Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Center for Genetics Medicine Research Children's National Research and Innovation Campus WashingtonDC United States Department of Pediatrics George Washington University School of Medicine and Health Sciences George Washington University WashingtonDC20037 United States Department of Biostatistics School of Public Health University of Washington SeattleWA98195 United States Brotman Baty Institute for Precision Medicine University of Washington SeattleWA98195 United States Department of Genome Sciences University of Washington SeattleWA98195 United States Department of Neurology Massachusetts General Hospital Harvard Medical School United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard CambridgeMA United States Department of Biomedical Data Science Stanford University School of Medicine Stanford CA94305 United States Department of Genetics

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype–phenotype associations, complex gene–environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order and emerging technologies, such as optical genome mapping and long-read DNA or RNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to a consortium such as GREGoR, which is focused on elucidating the underlying cause of rare unsolved genetic disorders. © 2023, CC BY-NC-ND.

关键词： RNA

The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning