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

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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...

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

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Probabilistic model checking for continuous-time markov chains via sequential bayesian inference

arXiv

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arXiv 2017年

作者： Milios, Dimitrios Sanguinetti, Guido Schnoerr, David Department of Data Science EURECOM School of Informatics University of Edinburgh SynthSys Centre for Synthetic and Systems Biology University of Edinburgh Centre for Integrative Systems Biology and Bioinformatics Department of Life Sciences Imperial College London

Probabilistic model checking for systems with large or unbounded state space is a challenging computational problem in formal modelling and its applications. Numerical algorithms require an explicit representation of ... 详细信息

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Crowdsourcing annotation system of object counting dataset for deep learning algorithm

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IOP Conference Series: Earth and Environmental science 2018年第1期195卷

作者： Tjeng Wawan Cenggoro Fidelson Tanzil Ayu Hidayah Aslamiah Ettikan Kandasamy Karuppiah Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 NVIDIA Corporation Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Deep Learning is currently the state-of-the-art technique for various Computer Vision tasks, including object counting. Despite of its high performance, Deep Learning requires a gigantic amount of training data to sho...

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Introducing evolutionary biologists to the analysis of big data: Guidelines to organize extended bioinformatics training courses

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Evolution: Education and Outreach 2018年第1期11卷 1-10页

作者： Faria, Rui Triant, Deborah Perdomo-Sabogal, Alvaro Overduin, Bert Bleidorn, Christoph Santana, Clara Isabel Bermudez Langenberger, David Indrischek, Henrike Aerts, Jan Engelhardt, Jan Engelken, Johannes Liebal, Katja Fasold, Mario Robb, Sofia Grath, Sonja Kolora, Sree Rohit Raj Carvalho, Tiago Salzburger, Walter Jovanovic, Vladimir Nowick, Katja CIBIO Centro de Investigação em Biodiversidade e Recursos Genéticos InBIO Laboratório Associado Universidade Do Porto Campus Agrário de Vairão Vairão 4485-661 Portugal IBE Institute of Evolutionary Biology (CSIC-UPF) Department of Experimental and Health Sciences Pompeu Fabra University Doctor Aiguader 88 Barcelona 08003 Spain Department of Animal and Plant Sciences University of Sheffield Sheffield United Kingdom Department of Biochemistry and Molecular Genetics University of Virginia Charlottesville 22908 VA United States TFome Research Group Bioinformatics Group Interdisciplinary Center of Bioinformatics Department of Computer Science University of Leipzig Härtelstrasse 16-18 Leipzig 04107 Germany Paul-Flechsig-Institute for Brain Research University of Leipzig Jahnallee 59 Leipzig 04109 Germany Faculty for Biology Chemistry and Pharmacy Freie Universität Berlin Königin-Luise-Straße 1-3 Berlin 14195 Germany Edinburgh Genomics University of Edinburgh Ashworth Laboratories King's Buildings Charlotte Auerbach Road Edinburgh EH9 3FL United Kingdom Museo Nacional de Ciencias Naturales Spanish Research Council (CSIC) José Gutiérrez Abascal 2 Madrid 28006 Spain German Centre for Integrative Biodiversity Research (IDiv) Halle-Jena-Leipzig Deutscher Platz 5e Leipzig 04103 Germany Grupo Rnomica Teórica y Computacional Departamento de Biologia Universidad Nacional de Colombia Bogota Colombia EcSeq Bioinformatics GmbH Sternwartenstraße 29 Leipzig 04103 Germany Computational Biology Department of Target Sciences GlaxoSmithKline Gunnels Wood Road Stevenage SG1 2NY United Kingdom Computational EvoDevo Group Department of Computer Science University of Leipzig Härtelstrasse 16-18 Leipzig 04107 Germany Bioinformatics Group Department of Computer Science University of Leipzig Härtelstrasse 16-18 Leipzig 04107 Germany Visual Data Analysis Lab ESAT/STADIUS KU Leuven Kasteelpark Arenberg 10 Leuven 3001 Belgium Imec KU Leuven Leuven Belgiu

Research in evolutionary biology has been progressively influenced by big data such as massive genome and transcriptome sequencing data, scalar measurements of several phenotypes on tens to thousands of individuals, a... 详细信息

Research in evolutionary biology has been progressively influenced by big data such as massive genome and transcriptome sequencing data, scalar measurements of several phenotypes on tens to thousands of individuals, as well as from collecting worldwide environmental data at an increasingly detailed scale. The handling and analysis of such data require computational skills that usually exceed the abilities of most traditionally trained evolutionary biologists. Here we discuss the advantages, challenges and considerations for organizing and running bioinformatics training courses of 2-3 weeks in length to introduce evolutionary biologists to the computational analysis of big data. Extended courses have the advantage of offering trainees the opportunity to learn a more comprehensive set of complementary topics and skills and allowing for more time to practice newly acquired competences. Many organizational aspects are common to any course, as the need to define precise learning objectives and the selection of appropriate and highly motivated instructors and trainees, among others. However, other features assume particular importance in extended bioinformatics training courses. To successfully implement a learning-by-doing philosophy, sufficient and enthusiastic teaching assistants (TAs) are necessary to offer prompt help to trainees. Further, a good balance between theoretical background and practice time needs to be provided and assured that the schedule includes enough flexibility for extra review sessions or further discussions if desired. A final project enables trainees to apply their newly learned skills to real data or case studies of their interest. To promote a friendly atmosphere throughout the course and to build a close-knit community after the course, allow time for some scientific discussions and social activities. In addition, to not exhaust trainees and TAs, some leisure time needs to be organized. Finally, all organization should be done while keeping

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Correction: LCE: an open web portal to explore gene expression and clinical associations in lung cancer

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Oncogene 2020年第3期39卷 718-719页

作者： Ling Cai ShinYi Lin Luc Girard Yunyun Zhou Lin Yang Bo Ci Qinbo Zhou Danni Luo Bo Yao Hao Tang Jeffrey Allen Kenneth Huffman Adi Gazdar John Heymach Ignacio Wistuba Guanghua Xiao John Minna Yang Xie Quantitative Biomedical Research Center Department of Clinical Sciences UT Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. Children's Medical Center Research Institute UT Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. Bioinformatics Core Facility UT Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. Hamon Center for Therapeutic Oncology Research University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. Department of Data Science University of Mississippi Medical Center 2500N State St Jackson MS 39216 USA. Department of Pathology National Cancer Center/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College 100021 Beijing China. Department of Pathology University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. Department of Thoracic/Head and Neck Medical Oncology University of Texas MD Anderson Cancer Center Houston TX 77005 USA. Department of Translational Molecular Pathology University of Texas MD Anderson Cancer Center Houston TX 77030 USA. Quantitative Biomedical Research Center Department of Clinical Sciences UT Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. Guanghua.Xiao@UTSouthwestern.edu. Bioinformatics Core Facility UT Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. Guanghua.Xiao@UTSouthwestern.edu. Simmons Cancer Center University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. Guanghua.Xiao@UTSouthwestern.edu. Hamon Center for Therapeutic Oncology Research University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. John.Minna@UTSouthwestern.edu. Simmons Cancer Center University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas TX 75390 USA. John.Minna@UTSouthwestern.edu. Department of Pharmacology University of Texas Southwestern Medical C

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

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2MO First-line (1L) nivolumab (NIVO) + ipilimumab (IPI) in metastatic non-small cell lung cancer (mNSCLC): Clinical outcomes and biomarker analyses from CheckMate 592

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Immuno-Oncology and Technology 2022年 16卷 100107-100107页

作者： S. Gettinger M. Schenker J. De Langen J.R. Fischer D. Morgensztern T-E. Ciuleanu T. Beck J. De Castro Carpeno C. Schumann X. Yang B. Telivala K. Deschepper E. Nadal K. Schalper T. Spires D. Balli A. Nassar S. Karam A. Bhingare D.R. Spigel Thoracic Medical Oncology Yale Comprehensive Cancer Center Yale University New Haven CT USA Department of Medical Oncology Sf Nectarie Oncology Center Craiova Dolj Romania Department of Thoracic Oncology The Netherlands Cancer Institute Antoni van Leeuwenhowek Hospital Amsterdam Netherlands Department of Oncology Lugenklinik Löwenstein GmbH Löwenstein Germany Division of Oncology Washington University St. Louis MO USA Medical Oncology IOCN - The Oncology Institute Prof. Dr. Ion Chiricuta Cluj-Napoca Romania Oncology Department Highlands Oncology Springdale AR USA Department of Medicine Hospital Universitario La Paz Madrid Spain Pulmonary Medicine Klinikverbund Kempten Kempten Germany Hematology & Medical Oncology Bon Secours Saint Francis Center Greenville SC USA Hematology & Medical Oncology Cancer Specialists of North Florida Jacksonville FL USA Lung Diseases Vitaz Sint-Niklaas Belgium Thoracic Tumors Unit Catalan Institute of Oncology Barcelona Barcelona Spain Department of Pathology Yale School of Medicine Yale University New Haven CT USA Translational Medicine Bristol Myers Squibb Princeton NJ USA Translational Bioinformatics - Informatics and Predictive Sciences Bristol Myers Squibb Princeton NJ USA Oncology Clinical Development Bristol Myers Squibb Princeton NJ USA Global Oncology Drug Development Bristol Myers Squibb Princeton NJ USA Global Biometrics and Data Science Bristol Myers Squibb Princeton NJ USA Medical Oncology Tennessee Oncology Nashville TN USA

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Corrigendum to "Identification of Differential Tumor Subtypes of T1 Bladder Cancer" [Eur. Urol. 78 (2020) 533-537]

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European urology 2022年第2期81卷 e53页

作者： A Gordon Robertson Clarice S Groeneveld Brian Jordan Xiaoqi Lin Kimberly A McLaughlin Arighno Das Leigh Ann Fall Damiano Fantini Timothy J Taxter Lauren S Mogil Sia Viborg Lindskrog Lars Dyrskjøt David J McConkey Robert S Svatek Aurélien de Reyniès Mauro A A Castro Joshua J Meeks Dxige Research Inc. Courtenay BC Canada. Cartes d'Identité des Tumeurs Program Ligue Nationale Contre le Cancer Paris France Oncologie Moleculaire Institut Curie Equipe Labellisée Ligue Contre le Cancer Paris France. Department of Urology University of Washington Seattle WA USA. Department of Pathology Northwestern University Feinberg School of Medicine Chicago IL USA. Departments of Urology Biochemistry and Molecular Genetics Feinberg School of Medicine Northwestern University Chicago IL USA. Tempus Labs Inc. Chicago IL USA. Center for Translational Data Science University of Chicago Chicago IL USA. Department of Molecular Medicine Aarhus University Hospital Aarhus Denmark. Johns Hopkins Greenberg Bladder Cancer Institute Brady Urological Institute Johns Hopkins University Baltimore MD USA. Experimental Developmental Therapeutics Program UT Health MD Anderson San Antonio TX USA Department of Urology UT Health San Antonio San Antonio TX USA. Cartes d'Identité des Tumeurs Program Ligue Nationale Contre le Cancer Paris France. Bioinformatics and Systems Biology Laboratory Federal University of Paraná Curitiba Brazil. Departments of Urology Biochemistry and Molecular Genetics Feinberg School of Medicine Northwestern University Chicago IL USA Jesse Brown VA Medical Center Chicago IL USA. Electronic address: joshua.meeks@northwestern.edu.

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TREM2 Burden associates solely with Alzheimer’s Disease, and decreases the Chance to become a Centenarian

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Alzheimer's & Dementia 2023年第S12期19卷

作者： Janna I.R. Dijkstra Lisa Vermunt Geert Jan Biessels den Anouk Braber Jurgen A.H.R. Claassen Peter Paul De Deyn Mohsen Ghanbari Martijn Huisman Marc Hulsman Inez H.G.B. Ramakers Marcel J. T. Reinders Harro Seelaar Niccoló Tesi Pieter Jelle Visser Wiesje M. van der Flier Charlotte E. Teunissen Henne Holstege Sven J Van Der Lee Amsterdam Neuroscience Amsterdam Netherlands Genomics of Neurodegenerative Diseases and Aging Human Genetics Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Amsterdam Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Neurochemistry Laboratory Amsterdam UMC Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Netherlands Department of Neurology UMC Utrecht Brain Center University Medical Center Utrecht Utrecht Netherlands Department of Biological Psychology Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Cardiovascular Sciences University of Leicester Leicester United Kingdom Department of Geriatrics & Radboudumc Alzheimer Center Radboud University Medical Center Nijmegen Netherlands Laboratory of Neurochemistry and Behaviour University of Antwerp Antwerp Belgium University Medical Center Groningen Neurology and Alzheimer Center Groningen Netherlands Department of Neurology and Memory Clinic Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken Antwerp Antwerp Belgium Department of Epidemiology Erasmus Medical Centre Rotterdam Netherlands Aging & Later Life Amsterdam Public Health Amsterdam Amsterdam Netherlands Department of Epidemiology & Data Science Amsterdam UMC location Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Sociology Amsterdam UMC location Vrije Universiteit Amsterdam Amsterdam Netherlands Genomics of Neurodegenerative Diseases and Aging Human Genetics Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Alzheimer Center Limburg School for Mental Health and Neuroscience (MHeNs) Maastricht University Maastricht Netherlands Delft Bioinformatics Lab Delft University of Technology Delft Delft Netherlands Department of Neurology Erasmus Medical Center Rotterdam Rotterdam Netherlands Department of Neurobiology Care Sciences and Society Division of Neurog

Background Rare TREM2 variants are major risk factors for Alzheimer’s disease (AD), but TREM2 variants may also confer a higher risk for other neurodegenerative diseases and a lower probability for cognitively health...

Background Rare TREM2 variants are major risk factors for Alzheimer’s disease (AD), but TREM2 variants may also confer a higher risk for other neurodegenerative diseases and a lower probability for cognitively healthy aging. We studied the TREM2 burden associated with different types of dementia and cognitively healthy centenarians (CHCs). Method We included participants of the 100-Plus Study, Amsterdam Dementia Cohort, Longitudinal Aging Study Amsterdam and various Dutch memory clinics. TREM2 variants (R47H, R62H, Q33X, R62C, T96K) were genotyped with a high density array or imputed using standard methods (TOPMED reference panel). To avoid family bias and ethnic differences, related individuals and those of non-European ancestry were excluded. Frequencies of individual variants and the burden of TREM2 variants were compared in AD dementia, dementia with Lewy Bodies (DLB), frontotemporal dementia (FTD), cognitively normal controls and CHCs (correcting for 5 principal components). Result We identified 242 carriers of TREM2 mutations and 6,683 controls without a TREM2 mutation with a mean age of 69 years (SD ±14 years), 50% female (Table 1). TREM2 variants were significantly associated with AD dementia (OR 1.4, 95%CI 1.1-1.8, p = 0.01) (Table 2). R47H conferred the highest risk for AD (OR 2.8, 95%CI 1.4-5.6, p = 0.004), compared to 1.3-fold increased risk of R62H (95%CI 0.9-1.7, p = 0.11). The carrier frequency was not increased in DLB (OR 1.4, 95%CI 0.7-2.6, p = 0.34) and was not increased in FTD compared to controls (OR 0.59, 95%CI 0.3-1.3, p = 0.18). Of interest, the allele frequency of TREM2 variants in centenarians is half of the expected frequency (OR = 0.46, 95%CI 0.1-1.1, p = 0.10), and only concerns R62H. Conclusion We replicate the association of TREM2 with increased risk of AD. In contrast to previous reports no association was observed with DLB or FTD. Cognitively healthy centenarians show a negative trend for TREM2 allele frequency suggesting that carryin

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Digital is the New Blood: Enabling the Present and the Future

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Alzheimer's & Dementia 2023年第S21期19卷

作者： Rhoda Au Zachary T Popp Spencer Low Phillip H Hwang Ileana De Anda-Duran Stephanie Li Salman Rahman Huitong Ding Akwaugo Igwe Cody Karjadi Ting Fang Alvin Ang Sherral A. Devine Ashita S Gurnani Jesse B. Mez Lindsay A. Farrer Preeti Sunderaraman Honghuang Lin Vijaya B. Kolachalama Boston University School of Public Health Boston MA USA Framingham Heart Study Boston University School of Medicine Boston MA USA Davos Alzheimer’s Collaborative Philadelphia PA USA Department of Epidemiology Boston University School of Public Health Boston MA USA Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University School of Public Heatlh Boston MA USA Tulane School of Public Health and Tropical Medicine New Orleans LA USA Slone Epidemiology Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framigham Heart Study - Boston University Framingham MA USA Boston University Bioinformatics Program Boston MA USA Departments of Medicine (Biomedical Genetics) Neurology Ophthalmology Biostatistics and Epidemiology Boston University Schools of Medicine and Public Health Boston MA USA Department of Neurology - Boston University Chobanian & Avedisian School of Medicine Boston MA USA University of Massachusetts Medical School Worcester MA USA Framingham Heart Study/Boston University BOSTON MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA College of Arts & Sciences Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background Digital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology-embedded sensors are replacing analog methods. Analyses of digital data extend beyond current a...

Background Digital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology-embedded sensors are replacing analog methods. Analyses of digital data extend beyond current applications, analogous to previously stored blood currently be used for omics/biomarker research. Today digital technologies are used to measure Alzheimer’s disease (AD)-related clinical symptoms (e.g., cognition, function, and behavior), but future possibilities remain to be determined. Importantly, digital data collection can go beyond current intermittent sampling by employing internet-connected applications and devices that allow real-time monitoring and accurate detection of changes, even within a widely heterogeneous spectrum. Method Beginning in 2005, the Framingham Heart Study (FHS) began digitally recording participants’ spoken responses to neuropsychological (NP) test questions, and in 2011 extended to a digital pen to record written responses. The emergence of wearable devices and smartphone applications has led to an expanded use of digital technologies beyond recordings during NP assessments. The Boston University Alzheimer’s Disease Research Center (BU ADRC) leverages digital sensors in multiple internet-connected devices that longitudinally collect digital data, in both in-person and remote settings. Result The first publication involving FHS digital data was in 2016; the remaining (n> 17) have been from 2020-current. Digital data measuring cognition, function and behavior are being collected through the BU ADRC, where results to date indicate nearly 100% and 75% uptake of active and passive engagement applications, respectively, and 95% uptake for wearable devices (n = 60). Longitudinal assessments include > 3 months for ∼50 participants; > 1 year for n∼21 participants. Conclusion Opportunities for improved trial outcome measurement are evident in the broad range of outcomes digital sensors capture beyond traditional research methodologi

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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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