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Author Correction: Conserved and divergent gene regulatory programs of the mammalian neocortex

Nature 2024年第7996期625卷 E26页

作者： Nathan R Zemke Ethan J Armand Wenliang Wang Seoyeon Lee Jingtian Zhou Yang Eric Li Hanqing Liu Wei Tian Joseph R Nery Rosa G Castanon Anna Bartlett Julia K Osteen Daofeng Li Xiaoyu Zhuo Vincent Xu Lei Chang Keyi Dong Hannah S Indralingam Jonathan A Rink Yang Xie Michael Miller Fenna M Krienen Qiangge Zhang Naz Taskin Jonathan Ting Guoping Feng Steven A McCarroll Edward M Callaway Ting Wang Ed S Lein M Margarita Behrens Joseph R Ecker Bing Ren Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA. Center for Epigenomics University of California San Diego School of Medicine La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA. Division of Biological Sciences University of California San Diego La Jolla CA USA. Computational Neurobiology Laboratory The Salk Institute for Biological Studies La Jolla CA USA. Department of Genetics The Edison Family Center for Genome Sciences & Systems Biology Washington University School of Medicine St Louis MO USA. Princeton Neuroscience Institute Princeton University Princeton NJ USA. Department of Genetics Harvard Medical School Boston USA. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. McGovern Institute for Brain Research Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA. Allen Institute for Brain Science Seattle WA USA. Systems Neurobiology Laboratories The Salk Institute for Biological Studies La Jolla CA USA. Department of Neurosciences University of California San Diego La Jolla CA USA. McDonnell Genome Institute Washington University School of Medicine St. Louis MO USA. Department of Neurological Surgery University of Washington Seattle WA USA. Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA. ecker@salk.edu. Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA. ecker@salk.edu. Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA. biren@ucsd.edu. Center for Epigenomics University of California San Diego School of Medicine La Jolla CA USA. biren@ucsd.edu. Institute of Genomic Medicine Moores Cancer C

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Void distributions reveal structural link between jammed packings and protein cores

arXiv

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arXiv 2018年

作者： Treado, John D. Mei, Zhe Regan, Lynne O'Hern, Corey S. Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction φ ≈ 0.56, which is the same as dense, random packing of amino acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. By measuring the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation, we show that void percolation in packings of amino acid-shaped particles and protein cores belong to the same universality class, which is different from that for void percolation in jammed sphere packings. We propose that the connected void regions of proteins are a defining feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations. Copyright © 2018, The Authors. All rights reserved.

关键词： Amino acids

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Correction to: Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes

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Genome medicine 2021年第1期13卷 139页

作者： David Z Pan Zong Miao Caroline Comenho Sandhya Rajkumar Amogha Koka Seung Hyuk T Lee Marcus Alvarez Dorota Kaminska Arthur Ko Janet S Sinsheimer Karen L Mohlke Nicholas Mancuso Linda Liliana Muñoz-Hernandez Miguel Herrera-Hernandez Maria Teresa Tusié-Luna Carlos Aguilar-Salinas Kirsi H Pietiläinen Jussi Pihlajamäki Markku Laakso Kristina M Garske Päivi Pajukanta Department of Human Genetics David Geffen School of Medicine at UCLA Los Angeles USA. Bioinformatics Interdepartmental Program UCLA Los Angeles USA. Computational and Systems Biology Interdepartmental Program UCLA Los Angeles USA. Institute of Public Health and Clinical Nutrition University of Eastern Finland Kuopio Finland. Department of Medicine David Geffen School of Medicine at UCLA Los Angeles USA. Department of Computational Medicine David Geffen School of Medicine at UCLA Los Angeles USA. Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA. Center for Genetic Epidemiology Department of Preventative Medicine Keck School of Medicine University of Southern California Los Angeles USA. Tecnologico de Monterrey Escuela de Medicina y Ciencias de la Salud Ave. Morones Prieto 3000 Monterrey N.L. México 64710. Unidad de Investigación de Enfermedades Metabólicas Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Departamento de Endocrinología y Metabolismo del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Departamento de Cirugía Instituto Nacional de Ciencias Médicas y Nutrición Mexico City Mexico. Unidad de Biología Molecular y Medicina Genómica Instituto de Investigaciones Biomédicas UNAM/ Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Obesity Research Unit Research Program for Clinical and Molecular Metabolism Faculty of Medicine University of Helsinki Helsinki Finland. Obesity Center Endocrinology Abdominal Center Helsinki University Central Hospital and University of Helsinki Helsinki Finland. Department of Medicine Endocrinology and Clinical Nutrition Kuopio University Hospital Kuopio Finland. Department of Medicine University of Eastern Finland and Kuopio University Hospital Kuopio Finland. Department of Human Genetics David Geffen School of Medicine at UCLA Los Angeles USA. ppajukant

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Modeling global dynamics from local snapshots with deep generative neural networks

arXiv

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arXiv 2018年

作者： Gigante, Scott Van Dijk, David Moon, Kevin R. Strzalkowski, Alexander Wolf, Guy Krishnaswamy, Smita Computational Biology and Bioinformatics Program Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Mathematics and Statistics Utah State University LoganUT United States Department of Computer Science Princeton University PrincetonNJ United States Department of Mathematics and Statistics Universitéde Montreacuteal MontéalQC Canada

Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describe analytically, "snapshot" measurements that sample the output of the system are often available. In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. DyMoN is a neural network framework trained as a deep generative Markov model whose next state is a probability distribution based on the current state. DyMoN is trained using samples of current and nextstate pairs, and thus does not require longitudinal measurements. We show the advantage of DyMoN over shallow models such as Kalman filters and hidden Markov models, and other deep models such as recurrent neural networks in its ability to embody the dynamics (which can be studied via perturbation of the neural network) and generate longitudinal hypothetical trajectories. We perform three case studies in which we apply DyMoN to different types of biological systems and extract features of the dynamics in each case by examining the learned model. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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A27 Exploring novel mechanisms of HIV-2 mutagenesis

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Virus Evolution 2018年第SUPPL_1期4卷

作者： Meissner, M E Baliga, S Roth, M Baller, J Mansky, L M Institute for Molecular Virology Madison WI USA Molecular Cellular Developmental Biology and Genetics Graduate Program Saint-Paul MN USA Bioinformatics and Computational Biology Graduate Program Minneapolis MN USA Minnesota Supercomputing Institute Minneapolis MN USA University of Minnesota Minneapolis MN USA

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Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts

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The Lancet Respiratory Medicine 2020年第7期8卷 696-708页

作者： Moll, Matthew Sakornsakolpat, Phuwanat Shrine, Nick Hobbs, Brian D DeMeo, Dawn L John, Catherine Guyatt, Anna L McGeachie, Michael J Gharib, Sina A Obeidat, Ma'en Lahousse, Lies Wijnant, Sara R A Brusselle, Guy Meyers, Deborah A Bleecker, Eugene R Li, Xingnan Tal-Singer, Ruth Manichaikul, Ani Rich, Stephen S Won, Sungho Kim, Woo Jin Do, Ah Ra Washko, George R Barr, R Graham Psaty, Bruce M Bartz, Traci M Hansel, Nadia N Barnes, Kathleen Hokanson, John E Crapo, James D Lynch, David Bakke, Per Gulsvik, Amund Hall, Ian P Wain, Louise Soler Artigas, María Jackson, Victoria E Strachan, David P Hui, Jennie James, Alan L Kerr, Shona M Polasek, Ozren Vitart, Veronique Marten, Jonathan Rudan, Igor Kähönen, Mika Surakka, Ida Gieger, Christian Karrasch, Stefan Rawal, Rajesh Schulz, Holger Deary, Ian J Harris, Sarah E Enroth, Stefan Gyllensten, Ulf Imboden, Medea Probst-Hensch, Nicole M Lehtimäki, Terho Raitakari, Olli T Langenberg, Claudia Luan, Jian'an Wareham, Nick Zhao, Jing Hua Hayward, Caroline Murray, Alison Porteous, David J Smith, Blair H Jarvelin, Marjo-Riitta Wielscher, Matthias Joshi, Peter K Kentistou, Katherine A Timmers, Paul RHJ Wilson, James F Cook, James P Lind, Lars Mahajan, Anubha Morris, Andrew P Ewert, Ralf Homuth, Georg Stubbe, Beate Weiss, Stefan Zeggini, Eleftheria Weiss, Scott T Silverman, Edwin K Dudbridge, Frank Tobin, Martin D Cho, Michael H Channing Division of Network Medicine Brigham and Women's Hospital Boston MA United States Division of Pulmonary and Critical Care Medicine Brigham and Women's Hospital Boston MA United States Department of Medicine Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Genetic Epidemiology Group Department of Health Sciences University of Leicester Leicester United Kingdom Computational Medicine Core Center for Lung Biology Department of Medicine University of Washington Seattle WA United States Division of Pulmonary Critical Care and Sleep Medicine Department of Medicine University of Washington Seattle WA United States Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle WA United States University of British Columbia Center for Heart Lung Innovation St Paul's Hospital Vancouver BC Canada Department of Epidemiology Erasmus Medical Centre Rotterdam Netherlands Department of Respiratory Medicine Erasmus Medical Centre Rotterdam Netherlands Department of Bioanalysis Faculty of Pharmaceutical Sciences Ghent University Ghent Belgium Department of Respiratory Medicine Ghent University Hospital Ghent Belgium Department of Medicine University of Arizona Tucson AZ United States GlaxoSmithKline Research and Development Collegeville PA United States Center for Public Health Genomics University of Virginia Charlottesville VA United States Department of Public Health Sciences University of Virginia Charlottesville VA United States Department of Public Health Sciences Graduate School of Public Health Seoul National University Seoul South Korea Interdisciplinary Program of Bioinformatics College of National Sciences Seoul National University Seoul South Korea Institute of Health and Environment Seoul National University Seoul South Korea Department of Internal Medicine Kangwon National University Chuncheon South Korea Department of Medicine and Department of Epidemiology Columbia Universit

Background: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. Methods: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV₁ and FEV₁/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV₁/FVC <0·7 and FEV₁ <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth. Findings: The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74–1·88] and non-European (1·42 [1·34–1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56–9·72) in European ancestry and 4·83 (3·45–6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79–0·81] vs 0·76 [0·75–0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The po

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Author Correction: Examining the healthy human microbiome concept

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Nature reviews. Microbiology 2025年第3期23卷 206页

作者： Raphaela Joos Katy Boucher Aonghus Lavelle Manimozhiyan Arumugam Martin J Blaser Marcus J Claesson Gerard Clarke Paul D Cotter Luisa De Sordi Maria G Dominguez-Bello Bas E Dutilh Stanislav D Ehrlich Tarini Shankar Ghosh Colin Hill Christophe Junot Leo Lahti Trevor D Lawley Tine R Licht Emmanuelle Maguin Thulani P Makhalanyane Julian R Marchesi Jelle Matthijnssens Jeroen Raes Jacques Ravel Anne Salonen Pauline D Scanlan Andrey Shkoporov Catherine Stanton Ines Thiele Igor Tolstoy Jens Walter Bo Yang Natalia Yutin Alexandra Zhernakova Hub Zwart Joël Doré R Paul Ross APC Microbiome Ireland University College Cork Cork Ireland. School of Microbiology University College Cork Cork Ireland. Department of Anatomy and Neuroscience University College Cork Cork Ireland. Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark. Center for Advanced Biotechnology and Medicine Rutgers University Piscataway NJ USA. Department of Psychiatry and Neurobehavioural Science University College Cork Cork Ireland. Teagasc Food Research Centre and VistaMilk SFI Research Centre Moorepark Fermoy Moorepark Ireland. Centre de Recherche Saint Antoine Sorbonne Université INSERM Paris France. Department of Biochemistry and Microbiology Rutgers University New Brunswick NJ USA. Institute of Biodiversity Faculty of Biological Sciences Cluster of Excellence Balance of the Microverse Friedrich Schiller University Jena Jena Germany. Theoretical Biology and Bioinformatics Department of Biology Science for Life Utrecht University Utrecht The Netherlands. Université Paris-Saclay INRAE MetaGenoPolis (MGP) Jouy-en-Josas France. Department of Clinical and Movement Neurosciences University College London London UK. Department of Computational Biology Indraprastha Institute of Information Technology Delhi (IIIT-Delhi) New Delhi India. Département Médicaments et Technologies pour La Santé (DMTS) Université Paris-Saclay CEA INRAE MetaboHUB Gif-sur-Yvette France. Department of Computing University of Turku Turku Finland. Host-Microbiota Interactions Laboratory Wellcome Sanger Institute Hinxton UK. National Food Institute Technical University of Denmark Kgs. Lyngby Denmark. Université Paris-Saclay INRAE AgroParisTech MICALIS Jouy-en-Josas France. Department of Microbiology Faculty of Science Stellenbosch University Stellenbosch South Africa. Division of Digestive Diseases Department of Metabolism Digestion and Reproduction Imperial College London London UK. K

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Whole exome sequencing identifies clinically relevant mutational signatures in resected hepatocellular carcinoma

Liver Cancer International

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Liver Cancer International 2020年第1期1卷

作者： Jason Y. Chan Abner H. Lim Arnoud Boot Elizabeth Lee Cedric C.-Y. Ng Jing Y. Lee Vikneswari Rajasegaran Wei Liu Shane Goh Jing H. Hong Xiaoying Xu Lavina D. Bharwani Chung Y. Chan Alexander Y. F. Chung Peng C. Cheow Chee-Kiat Tan Choon K. Ho Kui H. Liau Winston W. L. Woon Jee K. Low Akhil Chopra Gilberto Lopes Steven G. Rozen Bin T. Teh Alex Y.-C. Chang Division of Medical Oncology National Cancer Centre Singapore Singapore SingHealth Duke-NUS Blood Cancer Centre Singapore Oncology Academic Clinical Program Duke-NUS Medical School Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Dr Jason Y. Chan Division of Medical Oncology National Cancer Centre Singapore 11 Hospital Dr Singapore 169610. Professor Steven G. Rozen Centre for Computational Biology Duke-NUS Medical School Singapore 8 College Rd Singapore 169857. Professor Alex Y. Chang Department of Medical Oncology Tan Tock Seng Hospital Singapore and Department of Oncology Johns Hopkins University 11 Jalan Tan Tock Seng Singapore 308433. Integrated Genomics Platform National Cancer Centre Singapore Singapore Laboratory of Cancer Epigenome National Cancer Centre Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Institute of Molecular and Cell Biology Singapore Department of Medical Oncology Tan Tock Seng Hospital Singapore Department of Hepatopancreatobiliary and Transplant Surgery Singapore General Hospital Singapore Department of Gastroenterology and Hepatology Singapore General Hospital Singapore Department of General Surgery Tan Tock Seng Hospital Singapore LIAU KH Specialist Clinic Mt Elizabeth Novena Hospital Singapore Sylvester Comprehensive Cancer Center University of Miami Coral Gables FL USA Integrated Biostatistics and Bioinformatics Programme Duke-NUS Medical School Singapore Johns Hopkins Singapore Singapore Johns Hopkins University Baltimore MD USA

Background & Aims Most patients develop recurrent disease despite curative surgery for hepatocellular carcinoma (HCC). No standard adjuvant therapy is available and molecular predictors of outcome are poorly understood. Methods We conducted a multicentre pilot study on patients with localized HCC following surgical resection. Patients received up to 6 months of oral gefitinib as adjuvant therapy. Clinical end points included recurrence-free survival (RFS) and overall survival (OS), and exploratory analyses were conducted from whole exome sequencing data. Results A total of 65 patients were screened for the study, of which 40 were eligible. The median age was 63 years (range, 44-80). Most patients (80%) were positive for hepatitis B or C. With a median follow-up of 4.5 years, the median RFS was 24 months. Median OS was not reached. High Child-Pugh score and advanced T-stage (3-4) were independent predictors for both OS and RFS. Mutational signatures for exposure to aristolochic acid (AA), as characterized by a majority of T:A > A:T mutations, were observed in 18 cases (55%). HCC without AA mutagenesis was associated with early recurrences or death within 2 years of surgery ( P = .037). HCC with high T > A mutations was associated with better OS (HR 0.29, 95% CI 0.09-0.90, P = .033). Conversely, HCC with high C > T mutations was associated with worse OS (HR 4.55, 95% CI 1.20-17.31, P = .026). Conclusions Mutational signatures may carry prognostic significance in HCC following curative resection. Patient outcomes with gefitinib as adjuvant therapy after resection for HCC were modest, highlighting the need for urgent research in this area of unmet clinical need. *** number, NCT00282100.

关键词： adjuvant therapy aristolochic acid mutational signatures prognostic biomarker tyrosine kinase inhibitor

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Jamming of Deformable Polygons

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Physical Review Letters 2018年第24期121卷 248003-248003页

作者： Arman Boromand Alexandra Signoriello Fangfu Ye Corey S. O’Hern Mark D. Shattuck Beijing National Laboratory for Condensed Matter Physics and CAS Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA School of Physical Sciences University of Chinese Academy of Sciences Beijing China Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of the City University of New York New York New York 10031 USA

We introduce the deformable particle (DP) model for cells, foams, emulsions, and other soft particulate materials, which adds to the benefits and eliminates deficiencies of existing models. The DP model combines the ability to model individual soft particles with the shape-energy function of the vertex model, and adds arbitrary particle deformations. We focus on 2D deformable polygons with a shape-energy function that is minimized for area a0 and perimeter p0 and repulsive interparticle forces. We study the onset of jamming versus particle asphericity, A=p02/4πa0, and find that the packing fraction grows with A until reaching A*=1.16 of the underlying Voronoi cells at confluence. We find that DP packings above and below A* are solidlike, which helps explain the solid-to-fluid transition at A* in the vertex model as a transition from tension- to compression-dominated regimes.

关键词： Jamming Monolayer films Molecular dynamics

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Erratum for Wang et al., "Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin"

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Microbiology spectrum 2022年第1期10卷 e0261021页

作者： Hsin-Yao Wang Chia-Ru Chung Chao-Jung Chen Ko-Pei Lu Yi-Ju Tseng Tzu-Hao Chang Min-Hsien Wu Wan-Ting Huang Ting-Wei Lin Tsui-Ping Liu Tzong-Yi Lee Jorng-Tzong Horng Jang-Jih Lu Department of Laboratory Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Ph.D. Program in Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Computer Science and Information Engineering National Central Universitygrid.37589.30 Taoyuan City Taiwan. Graduate Institute of Integrated Medicine China Medical Universitygrid.254145.3 Taichung Taiwan. Proteomics Core Laboratory China Medical Universitygrid.254145.3 Hospital Taichung Taiwan. Graduate Program in Biomedical Information Yuan-Ze University Taoyuan City Taiwan. Department of Information Management Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei City Taiwan. Graduate Institute of Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Division of Haematology/Oncology Department of Internal Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Biosensor Group Biomedical Engineering Research Center Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Pathology Kaohsiung Chang Gung Memorial Hospitalgrid.413801.f and Chang Gung University College of Medicine Kaohsiung Taiwan. School of Life and Health Sciences The Chinese University of Hong Kong Shenzhen China. Warshel Institute for Computational Biology The Chinese University of Hong Kong Shenzhen China. Department of Bioinformatics and Medical Engineering Asia University Taichung City Taiwan. School of Medicine Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Medical Biotechnology and Laboratory Science Chang Gung Universitygrid.145695.a Taoyuan City Taiwan.

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