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Trends in Allogeneic Stem Cell Transplantation for Myelofibrosis in Europe between 1995-2018: An EBMT Retrospective Analysis

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BLOOD 2020年 136卷 38-39页

作者： McLornan, Donal P. Eikema, Dirk-Jan Kroger, Nicolaus Koster, Linda Czerw, Tomasz Beelen, Dietrich W. Angelucci, Emanuele Robin, Marie Bornhaeuser, Martin Passweg, Jakob McQuaker, Grant Vitek, Antonin Finke, Jurgen, Sr. Blau, Igor W. Niittyvuopio, Riitta Platzbecker, Uwe Cornelissen, Jan J. Chevallier, Patrice Srour, Micha Stamatovic, Dragana Martinez-Lopez, Joaquin de Wreede, Liesbeth C. Hayden, Patrick J. Boluda, Juan Carlos Hernandez Yakoub-Agha, Ibrahim Department of Clinical Haematology Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom Department of Statistics Leiden University Medical Centre Leiden Netherlands Department of Stem Cell Transplantation University Hospital Hamburg-Eppendorf Hamburg Germany EBMT Data Office Leiden Department of Medical Statistics and Bioinformatics Leiden University Medical Center Leiden Netherlands Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch Gliwice Poland Department of Bone Marrow Transplantation University of Essen Essen Germany Hematology and Transplant Unit IRCCS Ospedale Policlinico San Martino Genova Italy Hôpital Saint-Louis APHP Paris France Universitatsklinikum Cad GustavCarus an der Technischen Universltat Dresden Dresden Germany Hematology University Hospital Basel Basel Switzerland Beatson West of Scotland Cancer Centre Glasgow GBR Hematology Service Institute of Hematology and Blood Transfusion Prague Czech Republic PRAGUE Czech Republic University of Freiburg Freiburg Germany Dept. of Hematology Oncology and Stem Cell Transplantation Freiburg Germany Charité Universitätsmedizin Berlin Hematology Oncology and Tumor Immunology Berlin Germany Helsinki Hospital HELSINKI FIN Medical Clinic and Policlinic 1 Hematology and Cellular Therapy University Hospital Leipzig Leipzig Germany Department of Hematology Erasmus MC Cancer Institute Rotterdam Netherlands Hematology Clinic CHU Nantes Nantes France Hôpital Huriez CHU de Lille Lille France Military Medical Academy Belgrade SRB Univ Lille CHU de Lille INFINITE INSERM U1286 59000 Lille LILLE France

Aim: Dynamic assessment of trends over time in patient- and transplant-specific characteristics and outcomes for patients undergoing 1 st allogeneic haematopoietic cell transplant (allo-HCT) for Myelofibrosis (MF). Methods and Results: A total of 4142 MF patients were analysed who underwent allo-HCT between 1995-2018 (24-year period) across 278 centres based on data reported to the European Society for Blood and Marrow Transplantation. For analysis, 4 cohorts were considered based on year of allo-HCT: <2006 n=389 (9.4%), 2006-2010 n=910 (22%), 2011-2014 n=1148 (27.7%) and 2015-2018 n=1695 (40.9%). A steady increase in MF allo-HCT activity over time was apparent paralleled with increasing numbers of participating transplant centres. A total of 2603 (62.8%) patients were male, 3239 (78.2%) had Primary MF, 409 (9.9%) and 494 (11.9%) post-Polycythaemia Vera and post-Essential Thrombocythaemia MF, respectively. An increased median interval (lnterquartile range (IQR)) between diagnosis and transplant was evident over time (<2006 median 20.8 (8.9-62.2) months versus 36.2 (11.6-107.5) months in 2015-2018 period;p<0.001), potentially reflecting increased availability of therapeutics in recent eras (untreated patients <2006= 59.4% versus 2015-2018=23.7%). Median recipient age (IQR) increased over time by ~ a decade between earliest cohort and most recent: <2006, 49.4 years (43.1-55.3) versus 59.3 years (53.4-64.8)2015-2018. Prior to 2006, patients >60 years accounted for 8.7% of adults undergoing allo-HCT whereas for 2015-2018 this was 47%. Over time, increasing number of patients with a Karnofsky performance status (KPS) <90 underwent allo-HCT (<2006=19.7% versus 36.1% 2015-18; p<0.001). Peripheral Blood (PB) was the predominant stem cell source and utilisation increased over time, accounting for 74.8% <2006 and 92.2% within 2015-2018 cohort. Cord blood utilisation was limited to <1% throughout the 24-year study period. Significant shifts towards use of unrelated donors (URD

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Deep learning for imbalance data classification using class expert generative adversarial network

arXiv

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arXiv 2018年

作者： Fanny Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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An Evaluation of Deep Neural Network Performance on Limited Protein Phosphorylation Site Prediction data

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Procedia Computer Science 2019年 157卷 25-30页

作者： Favorisen Rosyking Lumbanraja Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Department of Computer Science Faculty of Mathematics and Natural Science University of Lampung Jalan Prof. Dr. Sumantri Brojonegoro No.17 35145 Bandar Lampung Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

One of the common and important post-translational modification (PTM) types is phosphorylation. Protein phosphorylation is used to regulate various enzyme and receptor activations which include signal pathways. There have been many significant studies conducted to predict phosphorylation sites using various machine learning methods. Recently, several researchers claimed deep learning based methods as the best methods for phosphorylation sited prediction. However, the performance of these methods were backed up with the massive training data used in the researches. In this paper, we study the performance of simple deep neural network on the limited data generally used prior to deep learning employment. The result shows that a deep neural network can still achieve comparable performance in the limited data settings.

关键词： Phosphorylation Site Prediction Protein Phosphorylation Deep Learning Deep Neural Network

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Radiogenomics Consortium Genome-Wide Association Study Meta-Analysis of Late Toxicity After Prostate Cancer Radiotherapy (vol 112, pg 179, 2020)

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JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE 2020年第6期112卷 654-654页

作者： Kerns, Sarah L. Fachal, Laura Dorling, Leila Barnett, Gillian C. Baran, Andrea Peterson, Derick R. Hollenberg, Michelle Hao, Ke Di Narzo, Antonio Ahsen, Mehmet Eren Pandey, Gaurav Bentzen, Soren M. Janelsins, Michelle Ellicot, Rebecca M. Pharoah, Paul D. Burnet, Neil G. Dearnaley, David P. Gulliford, Sarah L. Hall, Emma Sydes, Matthew R. Aguado-Barrera, Miguel E. Gomez-Caamano, Antonio Carballo, Ana M. Peleteiro, Paula Lobato-Busto, Ramon Stock, Richard Stone, Nelson N. Ostrer, Harry Usmani, Nawaid Singhal, Sandeep Tsuji, Hiroshi Imai, Takashi Saito, Shiro Eeles, Rosalind DeRuyck, Kim Parliament, Matthew Dunning, Alison M. Vega, Ana Rosenstein, Barry S. West, Catharine M. L. Departments of Radiation Oncology and Surgery University of Rochester Medical Center Rochester NY Department of Oncology Department of Public Health and Primary Care Department of Public Health and Primary Care Centre for Cancer Genetic Epidemiology Strangeways Research Laboratory University of Cambridge Cambridge UK Department of Oncology Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Biostatistics and Computational Biology University of Rochester Medical Center Rochester NY Department of Biostatistics and Computational Biology University of Rochester Medical Center Rochester NY Department of Computational Biology University of Rochester Rochester NY Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY Division of Biostatistics and Bioinformatics Department of Epidemiology and Public Health University of Maryland Greenebaum Cancer Center School of Medicine University of Maryland Baltimore Departments of Radiation Oncology and Surgery University of Rochester Medical Center Rochester NY Division of Cancer Sciences the University of Manchester Manchester Academic Health Science Centre Christie Hospital Manchester UK Centre for Cancer Genetic Epidemiology Strangeways Research Laboratory University of Cambridge Cambridge UK Department of Oncology Cambridge University Hospitals NHS Foundation Trust Cambridge UK Division of Cancer Sciences the University of Manchester Manches

Background A total of 10%-20% of patients develop long-term toxicity following radiotherapy for prostate cancer. Identification of common genetic variants associated with susceptibility to radiotoxicity might improve risk prediction and inform functional mechanistic *** We conducted an individual patient data meta-analysis of six genome-wide association studies (n = 3871) in men of European ancestry who underwent radiotherapy for prostate cancer. Radiotoxicities (increased urinary frequency, decreased urinary stream, hematuria, rectal bleeding) were graded prospectively. We used grouped relative risk models to test associations with approximately 6 million genotyped or imputed variants (time to first grade 2 or higher toxicity event). Variants with two-sided Pmeta less than 5 × 10−8 were considered statistically significant. Bayesian false discovery probability provided an additional measure of confidence. Statistically significant variants were evaluated in three Japanese cohorts (n = 962). All statistical tests were *** Meta-analysis of the European ancestry cohorts identified three genomic signals: single nucleotide polymorphism rs17055178 with rectal bleeding (Pmeta = 6.2 × 10−10), rs10969913 with decreased urinary stream (Pmeta = 2.9 × 10−10), and rs11122573 with hematuria (Pmeta = 1.8 × 10−8). Fine-scale mapping of these three regions was used to identify another independent signal (rs147121532) associated with hematuria (Pconditional = 4.7 × 10−6). Credible causal variants at these four signals lie in gene-regulatory regions, some modulating expression of nearby genes. Previously identified variants showed consistent associations (rs17599026 with increased urinary frequency, rs7720298 with decreased urinary stream, rs1801516 with overall toxicity) in new cohorts. rs10969913 and rs17599026 had similar effects in the photon-treated Japanese *** This study increases the understanding of the architecture of common gen

关键词： radiation therapy hematuria genes genome single nucleotide polymorphism urinary tract genetics prostate cancer rectal bleeding urinary frequency toxic effect genome-wide association study japanese imputation

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Generalized fitch graphs II: Sets of binary relations that are explained by edge-labeled trees

arXiv

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arXiv 2019年

作者： Hellmuth, Marc Seemann, Carsten R. Stadler, Peter F. Institute of Mathematics and Computer Science University of Greifswald Walther-Rathenau-Strasse 47 Greifswald D-17487 Germany Saarland University Center for Bioinformatics Building E 2.1 P.O. Box 151150 Saarbrücken D-66041 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig D-04103 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 Leipzig D-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine University of Leipzig Härtelstraße 16-18 Leipzig D-04107 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 Wien A-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia The Santa Fe Institute 1399 Hyde Park Rd. Santa Fe NM 87501 United States

Fitch graphs G = (X, E) are digraphs that are explained by {0/, 1}-edge-labeled rooted trees T with leaf set X: there is an arc (x, y) ∈ E if and only if the unique path in T that connects the last common ancestor lca(x, y) of x and y with y contains at least one edge with label "1". In practice, Fitch graphs represent xenology relations, i.e., pairs of genes x and y for which a horizontal gene transfer happened along the path from lca(x, y) to y. In this contribution, we generalize the concept of Fitch graphs and consider trees T that are equipped with edge-labeling λ : E → P(M) that assigns to each edge a subset M' ⊆ M of colors. Given such a tree, we can derive a map Ε(T,λ) (or equivalently a set of not necessarily disjoint binary relations), such that i ∈ Ε(T,λ)(x, y) (or equivalently (x, y) ∈ Ri) with x, y ∈ X, if and only if there is at least one edge with color i from lca(x, y) to y. The central question considered here: Is a given map Ε a Fitch map, i.e., is there there an edge-labeled tree (T, λ) with Ε(T,λ) = Ε, and thus explains Ε? Here, we provide a characterization of Fitch maps in terms of certain neighborhoods and forbidden submaps. Further restrictions of Fitch maps are considered. Moreover, we show that the least-resolved tree explaining a Fitch map is unique (up to isomorphism). In addition, we provide a polynomial-time algorithm to decide whether Ε is a Fitch map and, in the affirmative case, to construct the (up to isomorphism) unique least-resolved tree (T∗, λ∗) that explains Ε.MSC Codes 68R01, 05C05, 92D15 Copyright © 2019, The Authors. All rights reserved.

关键词： Phylogenetic

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Author Correction: Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer

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NPJ breast cancer 2023年第1期9卷 11页

作者： Shivangi Yadav Ranya Virk Carolina H Chung Mariana Bustamante Eduardo David VanDerway Duojiao Chen Kirsten Burdett Hongyu Gao Zexian Zeng Manish Ranjan Gannon Cottone Xiaoling Xuei Sriram Chandrasekaran Vadim Backman Robert Chatterton Seema Ahsan Khan Susan E Clare Department of Surgery Feinberg School of Medicine Northwestern University Chicago IL 60611 USA. Department of Biomedical Engineering Northwestern University Evanston IL 60208-2850 USA. Department of Biomedical Engineering University of Michigan Ann Arbor MI 48109 USA. Center of for Medical Genomics Indiana University School of Medicine Indianapolis IN 46202 USA. Department of Preventive Medicine Northwestern University Chicago IL 60611 USA. Department of Data Sciences Dana Farber Cancer Institute Harvard T.H. Chan School of Public Health Boston MA 02215 USA. Program in Chemical Biology University of Michigan Ann Arbor MI 48109 USA. Rogel Cancer Center University of Michigan Medical School Ann Arbor MI 48109 USA. Center for Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI 48109 USA. Department of Obstetrics and Gynecology Northwestern University Feinberg School of Medicine Chicago IL 60611 USA. Department of Surgery Feinberg School of Medicine Northwestern University Chicago IL 60611 USA. s-khan2@northwestern.edu. Department of Surgery Feinberg School of Medicine Northwestern University Chicago IL 60611 USA. susan.clare@northwestern.edu.

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Comprehensive reanalysis for CNVs in ES data from unsolved rare disease cases results in new diagnoses

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npj Genomic Medicine 2024年第1期9卷 49页

作者： Demidov, German Yaldiz, Burcu Garcia-Pelaez, José de Boer, Elke Schuermans, Nika Van de Vondel, Liedewei Paramonov, Ida Johansson, Lennart F. Musacchia, Francesco Benetti, Elisa Bullich, Gemma Sablauskas, Karolis Beltran, Sergi Gilissen, Christian Hoischen, Alexander Ossowski, Stephan de Voer, Richarda Lohmann, Katja Oliveira, Carla Topf, Ana Vissers, Lisenka E. L. M. Zguro, Kristina Zara, Federico Zaganas, Ioannis Yépez, Vicente A. Wirth, Brunhilde Webb, David Verdin, Hannah Van Nieuwenhove, Erika van Montfrans, Joris van Heurck, Roxane van der Veken, Lars Valle, Laura Uva, Paolo Udd, Bjarne Trimouille, Aurelien Thompson, Rachel Tartaglia, Marco Sznajer, Yves Striano, Pasquale Steinke-Lange, Verena Spilioti, Martha Scudieri, Paolo Schröck, Evelin Schon, Katherine Scala, Marcello Savarese, Marco Santen, Gijs W. E. Rump, Andreas Ruivenkamp, Claudia A. L. Roos, Andreas Rooryck, Caroline Riva, Antonella Renieri, Alessandra Ratnaike, Thiloka Radio, Francesca Clementina Privitera, Flavia De la Paz, Manuel Posada Poppe, Bruce Polavarapu, Kiran Platzer, Konrad Peterlin, Borut Pérez-Dueñas, Belén Pauly, Martje Parrini, Elena Olimpio, Catarina Reghan, Mary O. de Benito, Daniel Natera Osorio, Andrés Nascimento Munell, Francina Münchau, Alexander Moortgat, Stéphanie Monestier, Olivier Molnar, Maria Judit Meunier, Colombine Mertes, Christian Mei, Davide Maystadt, Isabelle May, Patrick Maver, Ales Mathioudakis, Lambros Masclaux, Frédéric Mary, Sandrine Delgado, Beatriz Martínez Marcé-Grau, Anna Malaichamy, Sivasankar Macaya, Alfons Luknárová, Rebeka Martín, Estrella López Lochmüller, Hanns Leitão, Elsa Lederer, Damien Leavis, Helen Laner, Andreas Lacombe, Didier Krass, Leon Korff, Christian Kokosali, Evgenia Karakaya, Mert Karadurmus, Deniz Kapaki, Elisabeth Johari, Mridul Iacomino, Michele Huibers, Manon Horvath, Rita Holinski-Feder, Elke Hoffer, Mariette J. V. Herzog, Rebecca Hemelsoet, Dimitri Bouveret, Eva Hammar Haimel, Matthias Hackman, Peter Guerrini, Renzo Grimbacher, Bodo Greally, Marie Ghani, Hamidah Gagneur, Julien Institute of Medical Genetics and Applied Genomics University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics (IBMI) University of Tübingen Tübingen Germany Department of Human Genetics Radboud University Medical Center Nijmegen Netherlands Department of Clinical Genetics Maastricht University Medical Center Maastricht Netherlands i3S - Instituto de Investigação e Inovação em Saúde Rua Alfredo Allen 208 Porto 4200-135 Portugal IPATIMUP - Institute of Molecular Pathology and Immunology University of Porto Porto Portugal Faculty of Medicine University of Porto Porto Portugal Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Department of Clinical Genetics Erasmus MC University Medical Center Rotterdam Netherlands Center for Medical Genetics Ghent University Hospital Ghent Belgium Translational Neurosciences Faculty of Medicine and Health Sciences University of Antwerp Antwerp Belgium Laboratory of Neuromuscular Pathology Institute Born-Bunge University of Antwerp Antwerp Belgium Centro Nacional de Análisis Genómico (CNAG) C/Baldiri Reixac 4 Barcelona 08028 Spain Universitat de Barcelona (UB) Barcelona Spain University of Groningen University Medical Center Groningen Department of Genetics Groningen Netherlands Center for Human Technologies Italian Institute of Technology (IIT) Genova Italy Telethon Institute for Genetics and Medicine Pozzuoli (Napoli) 80078 Italy Department of Medical Biotechnologies Med Biotech Hub and Competence Center University of Siena Siena 53100 Italy Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania Departament de Genètica Microbiologia i Estadística Facultat de Biologia Universitat de Barcelona (UB) Barcelona Spain Radboud Institute for Molecular Life Sciences Nijmegen Netherlands Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI) Radboud University Medical Center Nijmegen N

We report the results of a comprehensive copy number variant (CNV) reanalysis of 9171 exome sequencing datasets from 5757 families affected by a rare disease (RD). The data reanalysed was extremely heterogeneous, having been generated using 28 different enrichment kits by 42 different research groups across Europe partnering in the Solve-RD project. Each research group had previously undertaken their own analysis of the data but failed to identify disease-causing variants. We applied three CNV calling algorithms to maximise sensitivity, and rare CNVs overlapping genes of interest, provided by four partner European Reference Networks, were taken forward for interpretation by clinical experts. This reanalysis has resulted in a molecular diagnosis being provided to 51 families in this sample, with ClinCNV performing the best of the three algorithms. We also identified partially explanatory pathogenic CNVs in a further 34 individuals. This work illustrates the value of reanalysing ES cold cases for CNVs. © The Author(s) 2024.

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Corrigendum to “Inhibition of colorectal cancer targets IL-6, CTLA-4, & B7-2 by Tislelizumab: Molecular docking, dynamics, & STRING protein-protein network analysis” [Inform Med Unlocked 41 (2023) 101323]

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Informatics in Medicine Unlocked 2023年 43卷

作者： Mahmoud Elkazzaz Mahmoud Abdalrahman Shahid Ullah Israa M. Shamkh Amr Ahmed Ted X. Wu Maii S. Elsharayidi Mohammad Shahbaz Khan Aziz-ur Rehman Marwa M. Lotfy Abdullah Haikal Tianshun Gao Department of Chemistry and Biochemistry Faculty of Science Damietta University Damietta 7952567 Egypt Science Department Milton Academy Milton MA 02186 USA Lowell Institute CPS Northeastern University 360 Huntington Ave Boston MA 02115 USA S Khan Lab Mardan Khyber Pakhtunkhwa Pakistan Chemo and Bioinformatics Lab Bio Search Research Institution BSRI Giza 12613 Egypt Public Health Department First Health Cluster Ministry of Health Riyadh 966-11 Saudi Arabia Central Public Health Laboratories Egyptian Ministry of Health Egypt Children's National Hospital Washington DC USA Keystone Pharmacogenomics LLC Bensalem PA 19020 USA Faculty of Pharmacy Zagazig University Zagazig 44511 Egypt Department of Pharmacognosy Faculty of Pharmacy Mansoura University Mansoura 35516 Egypt Big Data Center The Seventh Affiliated Hospital of Sun Yat-sen University Shenzhen 518107 PR China

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Author Correction: Single nucleotide polymorphisms are associated with strain-specific virulence differences among clinical isolates of Cryptococcus neoformans

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Nature communications 2025年第1期16卷 2510页

作者： Katrina M Jackson Thomas J Y Kono Jovany J Betancourt Yina Wang Kisakye D Kabbale Minna Ding Perry Kezh Grace Ha J Marina Yoder Sophie R Fulton Liliane Mukaremera Peter Tiffin Asiya Gusa David B Meya R Blake Billmyre Chaoyang Xue Kirsten Nielsen Department of Microbiology and Immunology University of Minnesota Minneapolis MN USA. Pathogen and Microbiome Institute Northern Arizona University Flagstaff AZ USA. Minnesota Supercomputing Institute University of Minnesota Minneapolis MN USA. Institute of Computational Cancer Biology at the University Hospital of Cologne University of Cologne Cologne Germany. Public Health Research Institute and Department of Microbiology Biochemistry and Molecular Genetics New Jersey Medical School Rutgers University Newark NJ USA. Infectious Diseases Institute and School of Medicine College of Health Sciences Makerere University Kampala Uganda. The African Center of Excellence in Bioinformatics and Data Intensive Sciences Kampala Uganda. Center for One Health Research Department of Biomedical Sciences and Pathology Virginia Tech University Blacksburg VA USA. Department of Biosciences Faculty of Health and Life Sciences Medical Research Council Centre for Medical Mycology at the University of Exeter Exeter UK. Department of Plant and Microbial Biology University of Minnesota St. Paul MN USA. Department of Molecular Genetics and Microbiology Duke University Durham NC USA. Division of Infectious Diseases and International Medicine University of Minnesota Minneapolis MN USA. Departments of Pharmaceutical and Biomedical Sciences/Infectious Disease College of Pharmacy/College of Veterinary Medicine University of Georgia Athens GA USA. Department of Microbiology and Immunology University of Minnesota Minneapolis MN USA. kirstennielsen@vt.edu. Center for One Health Research Department of Biomedical Sciences and Pathology Virginia Tech University Blacksburg VA USA. kirstennielsen@vt.edu.

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Author Correction: A comprehensive update to the Mycobacterium tuberculosis H37Rv reference genome

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Nature communications 2022年第1期13卷 7538页

作者： Poonam Chitale Alexander D Lemenze Emily C Fogarty Avi Shah Courtney Grady Aubrey R Odom-Mabey W Evan Johnson Jason H Yang A Murat Eren Roland Brosch Pradeep Kumar David Alland Ray V. Lourenco Center for the Study of Emerging and Re-emerging Pathogens Rutgers University-New Jersey Medical School Newark NJ USA. Public Health Research Institute Rutgers University-New Jersey Medical School Newark NJ USA. Department of Pathology Immunology and Laboratory Medicine New Jersey Medical School Rutgers-The State University of New Jersey Newark NJ USA. Department of Medicine University of Chicago Chicago IL USA. Committee on Microbiology University of Chicago Chicago IL USA. Department of Microbiology Biochemistry and Molecular Genetics Rutgers University-New Jersey Medical School Newark NJ USA. Division of Computational Biomedicine Boston University School of Medicine and Bioinformatics Program Boston University Boston MA USA. Bioinformatics Program Boston University Boston MA USA. Center for Data Science Rutgers University-New Jersey Medical School Newark NJ USA. Helmholtz Institute for Functional Marine Biodiversity (HIFMB) Oldenburg Germany. Bay Paul Center Marine Biological Laboratory Woods Hole MA USA. Institut Pasteur Université Paris Cité Unit for Integrated Mycobacterial Pathogenomics CNRS UMR 6047 Paris France. Ray V. Lourenco Center for the Study of Emerging and Re-emerging Pathogens Rutgers University-New Jersey Medical School Newark NJ USA. allandda@njms.rutgers.edu. Public Health Research Institute Rutgers University-New Jersey Medical School Newark NJ USA. allandda@njms.rutgers.edu.

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