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arXiv 2019年

作者： Dai, Hong-Ning Zheng, Zibin Zhang, Yan Faculty of Information Technology Macau University of Science and Technology China School of Data and Computer Science Sun Yat-sen University China Department of Informatics University of Oslo Norway Simula Metropolitan Center for Digital Engineering Norway

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges such as decentralization, poor interoperability, privacy and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as Blockchain of Things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for 5G beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area. Copyright © 2019, The Authors. All rights reserved.

关键词： Smart contract

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Analysis of time-to-event for observational studies: Guidance to the use of intensity models

arXiv

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arXiv 2020年

作者： Andersen, Per Kragh Perme, Maja Pohar van Houwelingen, Hans C. Cook, Richard J. Joly, Pierre Martinussen, Torben Taylor, Jeremy M.G. Abrahamowicz, Michal Therneau, Terry M. Section of Biostatistics University of Copenhagen Denmark Department of Biostatistics and Medical Informatics Medical faculty University of Ljubljana Slovenia Department of Biomedical Data Sciences Leiden University Netherlands Department of Statistics and Actuarial Science University of Waterloo Canada Inserm ISPED Bordeaux Populations Health Research Center University of Bordeaux France Department of Biostatistics University of Michigan United States Department of Epidemiology Biostatistics and Occupational Health McGill University Montreal Canada Division of Biomedical Statistics and Informatics Mayo Clinic Rochester United States

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative. Copyright © 2020, The Authors. All rights reserved.

关键词： Biographies

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Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

arXiv

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arXiv 2024年

作者： Evans, Matthew L. Bergsma, Johan Merkys, Andrius Andersen, Casper W. Andersson, Oskar B. Beltrán, Daniel Blokhin, Evgeny Boland, Tara M. Balderas, Rubén Castañeda Choudhary, Kamal Díaz, Alberto Díaz García, Rodrigo Domínguez Eckert, Hagen Eimre, Kristjan Montero, María Elena Fuentes Krajewski, Adam M. Mortensen, Jens Jørgen Duarte, José Manuel Nápoles Pietryga, Jacob Qi, Ji de Jesús Trejo Carrillo, Felipe Vaitkus, Antanas Yu, Jusong Zettel, Adam de Castro, Pedro Baptista Carlsson, Johan Cerqueira, Tiago F.T. Divilov, Simon Hajiyani, Hamidreza Hanke, Felix Jose, Kevin Oses, Corey Riebesell, Janosh Schmidt, Jonathan Winston, Donald Xie, Christen Yang, Xiaoyu Bonella, Sara Botti, Silvana Curtarolo, Stefano Draxl, Claudia Cobas, Luis Edmundo Fuentes Hospital, Adam Liu, Zi-Kui Marques, Miguel A.L. Marzari, Nicola Morris, Andrew J. Ong, Shyue Ping Orozco, Modesto Persson, Kristin A. Thygesen, Kristian S. Wolverton, Chris Scheidgen, Markus Toher, Cormac Conduit, Gareth J. Pizzi, Giovanni Gražulis, Saulius Rignanese, Gian-Marco Armiento, Rickard Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Matgenix SRL 185 Rue Armand Bury Gozée6534 Belgium École Polytechnique Fédérale de Lausanne Avenue de Forel 3 Lausanne1015 Switzerland Institute of Biotechnology Life Sciences Center Vilnius University Sauletekio av. 7 VilniusLT-10257 Lithuania SINTEF P.O. Box 4760 Torgarden TrondheimNO-7465 Norway Materials Design and Informatics unit Department of Physics Chemistry and Biology Linköping University Sweden Baldiri i Reixac 10-12 Barcelona08028 Spain Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Computational Atomic-Scale Materials Design Technical University of Denmark Kgs. Lyngby Denmark Av. Miguel de Cervantes 120 Complejo Industrial Chihuahua Chihuahua Chih. 31136 Mexico Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Center for Extreme Materials Duke University DurhamNC27708 United States École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Universidad Autónoma de Chihuahua Facultad de Ciencias Quimicas Chihuahua31125 Mexico Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of NanoEngineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093-0448 United States National Institute for Materials Science 1-2-1 Sengen Ibaraki Tsukuba305-0047 Japan Dassault Systèmes Germany GmbH Am Kabellager 11-13 Cologne51063 Germany CFisUC Department of Physics University of Coimbra Rua Larga Coimbra3004-516 Portugal Dassault Systèmes 22 Science Park CB4 0FJ United Kingdom Theory of Condensed Matter Cavendis

The Open databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development. © 2024, CC BY.

关键词： Electronic data interchange

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AI-Driven Antimicrobial Peptide Discovery: Mining and Generation

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Accounts of chemical research 2025年第12期58卷 2025 Jun 3页

作者： Paulina Szymczak Wojciech Zarzecki Jiejing Wang Yiqian Duan Jun Wang Luis Pedro Coelho Cesar de la Fuente-Nunez Ewa Szczurek Institute of AI for Health Helmholtz Zentrum Munich Neuherberg 85764 Germany. Faculty of Mathematics Informatics and Mechanics University of Warsaw Warsaw 02-097 Poland. Faculty of Electronics and Information Technology Warsaw University of Technology Warsaw 00-661 Poland. Institute of Microbiology Chinese Academy of Sciences Beijing 100101 China. Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai 200433 China. Centre for Microbiome Research School of Biomedical Sciences Queensland University of Technology Translational Research Institute Woolloongabba Queensland 4102 Australia. Centre for Data Science Queensland University of Technology Brisbane Queensland 4001 Australia. Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America. Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America. Department of Chemistry School of Arts and Sciences University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America. Penn Institute for Computational Science University of Pennsylvania Philadelphia Pennsylvania 19104 United States of America.

ConspectusThe escalating threat of antimicrobial resistance (AMR) poses a significant global health crisis, potentially surpassing cancer as a leading cause of death by 2050. Traditional antibiotic discovery methods have not kept pace with the rapidly evolving resistance mechanisms of pathogens, highlighting the urgent need for novel therapeutic strategies. In this context, antimicrobial peptides (AMPs) represent a promising class of therapeutics due to their selectivity toward bacteria and slower induction of resistance compared to classical, small molecule antibiotics. However, designing effective AMPs remains challenging because of the vast combinatorial sequence space and the need to balance efficacy with low toxicity. Addressing this issue is of paramount importance for chemists and researchers dedicated to developing next-generation antimicrobial *** intelligence (AI) presents a powerful tool to revolutionize AMP discovery. By leveraging AI, we can navigate the immense sequence space more efficiently, identifying peptides with optimal therapeutic properties. This Account explores the emerging application of AI in AMP discovery, focusing on two primary strategies: AMP mining, and AMP generation, as well as the use of discriminative methods as a valuable *** mining involves scanning biological sequences to identify potential AMPs. Discriminative models are then used to predict the activity and toxicity of these peptides. This approach has successfully identified numerous promising candidates, which were subsequently validated experimentally, demonstrating the potential of AI in AMP design and *** generation, on the other hand, creates novel peptide sequences by learning from existing data through generative modeling. This class of models optimizes for desired properties, such as increased activity and reduced toxicity, potentially producing synthetic peptides that surpass naturally occurring ones. Despite the risk of generating un

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Distinct virologic trajectories in chronic hepatitis B identify heterogeneity in response to nucleos(t)ide analogue therapy

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JHEP Reports 2025年第1期7卷 101229-101229页

作者： Wang, Tingyan Campbell, Cori Stockdale, Alexander J. Todd, Stacy McIntyre, Karl Frankland, Andrew Jaworski, Jakub Glampson, Ben Papadimitriou, Dimitri Mercuri, Luca Mayer, Erik Jones, Christopher R. Salih, Hizni Roadknight, Gail Little, Stephanie Noble, Theresa Várnai, Kinga A. Davis, Cai Heinson, Ashley I. George, Michael Borca, Florina English, Louise Romão, Luis Ramlakhan, David Barnes, Eleanor Matthews, Philippa C. Gelson, William Cooke, Graham S. Khakoo, Salim I. Nastouli, Eleni Davies, Jim Woods, Kerrie Ryder, Stephen Elsharkawy, Ahmed Easom, Nicholas Bernal, William Ahmad, Shazaad Macdonald, Douglas Stanworth, Simon Maynard, Suzanne MacNaughton, Heidi Kim, Yun Meneses, Josune Olza Roberts, Timothy Rogers, Heather Roberts, Lara Higgins, Finola Vilar, Javier Norris, Ruth Tilston, George Serafimova, Ilina Montague, Sarah Verheyden, Juliette Juurlink, Irene Jack, Kathryn Waldren-Glenn, Alex Poole, Lizzie Day, Victoria Reglar, Berit NIHR Oxford Biomedical Research Centre Oxford United Kingdom Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Clinical Infection Microbiology and Immunology Institute of Infection Veterinary and Ecological Sciences University of Liverpool Liverpool United Kingdom Tropical Infectious Diseases Unit Royal Liverpool Hospital Liverpool University Hospitals NHS Trust Liverpool United Kingdom Liverpool Clinical Laboratories Liverpool University Hospitals NHS Trust Liverpool United Kingdom Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom iCARE Secure Data Environment Digital Collaboration Space Imperial College Healthcare NHS Trust London United Kingdom Department of Surgery and Cancer Imperial College London London United Kingdom Department of Infectious Disease Imperial College London London United Kingdom NIHR Health Informatics Collaborative Oxford University Hospitals NHS Foundation Trust Oxford United Kingdom Southampton Emerging Therapies and Technologies Centre University Hospital Southampton NHS Foundation Trust Southampton United Kingdom Clinical Informatics Research Unit Faculty of Medicine University of Southampton Southampton United Kingdom NIHR University College London Hospitals Biomedical Research Centre London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Infection Immunity and Inflammation UCL Great Ormond Street Institute of Child Health London United Kingdom Department of Virology UCLH London United Kingdom School of Clinical and Experimental Sciences Faculty of Medicine University of Southampton Southampton United Kingdom Cambridge Liver Unit Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom Faculty of Medicine Department of Infectious Disease Imperial College London United Kingdom The Francis Crick Institute London United Kingdom Division of Infection and Immunity University College Lon

Background & Aims: The dynamics of HBV viral load (VL) in patients with chronic hepatitis B (CHB) on nucleos(t)ide analogue (NA) treatment and its relationship with liver disease are poorly understood. We aimed to study longitudinal VL patterns and their associations with CHB clinical outcomes. Methods: Utilising large scale, routinely collected electronic health records from six centres in England, collated by the National Institute for Health and Care Research Health informatics Collaborative (NIHR HIC), we applied latent class mixed models to investigate VL trajectory patterns in adults receiving NA treatment. We assessed associations of VL trajectory with alanine transaminase, and with liver fibrosis/cirrhosis. Results: We retrieved data from 1,885 adults on NA treatment (median follow-up 6.2 years, IQR 3.7–9.3 years), with 21,691 VL measurements (median 10 per patient, IQR 5–17). Five VL classes were identified from the derivation cohort (n = 1,367, discrimination: 0.93, entropy: 0.90): class 1 ‘long term suppression’ (n = 827, 60.5%), class 2 ‘timely virological suppression’ (n = 254, 18.6%), class 3 ‘persistent moderate viraemia’ (n = 140, 10.2%), class 4 ‘persistent high-level viraemia’ (n = 44, 3.2%), and class 5 ‘slow virological suppression’ (n = 102, 7.5%). The model demonstrated a discrimination of 0.93 and entropy of 0.88 for the validation cohort (n = 518). Alanine transaminase decreased variably over time in VL-suppressed groups (classes 1, 2, 5;all p <0.001), but did not significantly improve in those with persistent viraemia (classes 3, 4). Patients in class 5 had twofold increased hazards of fibrosis/cirrhosis compared with class 1 (adjusted hazard ratio, 2.00;95% CI, 1.33–3.02). Conclusions: Heterogeneity exists in virological response to NA therapy in CHB patients, with over 20% showing potentially suboptimal responses. Slow virological suppression is associated with liver disease progression. Impact and implications: Treatment recommendations f

关键词： Antiviral treatment Cirrhosis HBV Health informatics Collaborative (HIC) Latent class mixed models Liver fibrosis Longitudinal Nucleotide analogue Viral load

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System Evaluation of Ternary Error-Correcting Output Codes for Multiclass Classification Problems*

System Evaluation of Ternary Error-Correcting Output Codes f...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shigeichi Hirasawa Gendo Kumoi Hideki Yagi Manabu Kobayashi Masayuki Goto Tetsuya Sakai Hiroshige Inazumi Research Institute for Science and Engineering Waseda University Tokyo Japan School of Creative Science and Engineering Waseda University Tokyo Japan Graduate School of Informatics and Engineering the University of Electro-Communications Tokyo Japan Center for Data Science Waseda University Tokyo Japan School of Fundamental Science and Engineering Waseda University Tokyo Japan Faculty of Informatics Aoyama Gakuin University Kanagawa Japan

To solve multiple classification problems with M(≥ 3) categories, many studies have been devoted using N(≥ ⌈log 2 M⌉) binary ({0, 1}) classifiers, where these systems are known as binary Error-Correcting Output Codes (binary ECOC). As an extended version of the binary ECOC, the ternary ({0, * , 1}) ECOC have also been discussed, where ternary classifiers classify data into positive examples when the element is 1, into negative examples when the element is 0, and no classification when the element is * . In this paper, we discuss the ternary ECOC system from the view point of the system evaluation model based on rate-distortion function. First, we discuss a table of M code words with length N which is given by a ternary matrix W of M rows and N columns. Next, by leveraging the benchmark data for multiclass document classification which is widely used in Japan, the relationships between the probability of classification error P e and the number of the ternary classifiers N for a given M are experimentally investigated. In addition, by assuming the M-dimensional Normal distribution for a classification data model, the relationship between P e and N for a given M is also examined. Finally, we show by the system evaluation model that the ternary ECOC systems have desirable properties such as “Flexible”, “Elastic”, and “Effective Elastic”, when M becomes large.

关键词： Decoding Rate-distortion Distortion Error correction codes Information systems Matrix decomposition data models

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P902 Native American Mapuche ancestry is related to higher IBD risk in Chileans

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Journal of Crohn's and Colitis 2023年第SUPPLEMENT_1期17卷 i1015–i1016页

作者： Pérez, T Bustamante, M L Magne, F Travisany, D Alvarez-Lobos, M Hernandez-Rocha, C Azocar, L Aguilar, N Espino, A Estela, R Escobar, S Zazueta, A Baez, P Silva, V Arriagada, E Barahona, C Zollner, L Miquel, J F Lorenzo-Bermejo, J Pontificia Universidad Católica de Chile Department of Gastroenterology Santiago Chile Hospital San Borja Arriarán Department of Gastroenterology Santiago Chile University of Chile Faculty of Medicine- ICBM Santiago Chile Universidad de la Americas NIDS- Núcleo de Investigación en Data Science- Facultad de Ingeniería y Negocios Santiago Chile University of Chile Center for Mathematical Modeling - University of Chile ... Santiago Chile University of Heidelberg Statistical Genetics Research Group- Institute of Medical Biometry and Informatics Heidelberg Germany

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Towards a framework for the design, implementation and reporting of methodology scoping reviews

arXiv

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arXiv 2020年

作者： Martin, Glen P. Jenkins, David A. Bull, Lucy Sisk, Rose Lin, Lijing Hulme, William Wilson, Anthony Wang, Wenjuan Barrowman, Michael Sammut-Powell, Camilla Pate, Alexander Sperrin, Matthew Peek, Niels Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom NIHR Greater Manchester Patient Safety Translational Research Centre University of Manchester Manchester United Kingdom Manchester Epidemiology Centre versus Arthritis Centre for Musculoskeletal Research Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Centre for Biostatistics Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Adult Critical Care Manchester University Hospitals NHS Foundation Trust Manchester United Kingdom Department of Population Health Sciences Faculty of Life Science and Medicine King’s College London United Kingdom

Background In view of the growth of published papers, there is an increasing need for studies that summarise scientific research. An increasingly common review is a "Methodology scoping review", which provides a summary of existing analytical methods, techniques and software, proposed or applied in research articles, which address an analytical problem or further an analytical approach. However, guidelines for their design, implementation and reporting are limited. Methods Drawing on the experiences of the authors, which were consolidated through a series of face-to-face workshops, we summarise the challenges inherent in conducting a methodology scoping review and offer suggestions of best practice to promote future guideline development. Results We identified three challenges of conducting a methodology scoping review. First, identification of search terms;one cannot usually define the search terms a priori and the language used for a particular method can vary across the literature. Second, the scope of the review requires careful consideration since new methodology is often not described (in full) within abstracts. Third, many new methods are motivated by a specific clinical question, where the methodology may only be documented in supplementary materials. We formulated several recommendations that build upon existing review guidelines. These recommendations ranged from an iterative approach to defining search terms through to screening and data extraction processes. Conclusion Although methodology scoping reviews are an important aspect of research, there is currently a lack of guidelines to standardise their design, implementation and reporting. We recommend a wider discussion on this topic. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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