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Approved trends and product characteristics of digital therapeutics in four countries

NPJ digital medicine 2025年第1期8卷 308页

作者： Jun Liang Qichuan Fang Xiaoyi Jiao Peng Xiang Junhao Ma Zijiao Zhang Yongcheng Liu Yunfan He Yingjun Li Zhixu He Jianbo Lei Department of AI and IT Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China. School of Medical Technology and Information Engineering Zhejiang Chinese Medical University Hangzhou China. Center for Health Policy Studies School of Public Health Zhejiang University Hangzhou China. National Key Laboratory of Transvascular Implantable Devices Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China. Intelligent Medical Research Center Zhejiang University Institute of Computer Innovation Technology Hangzhou China. Asian Section Digital Therapeutics Alliance Hangzhou China. Department of Epidemiology and Health Statistics School of Public Health Hangzhou Medical College Hangzhou China. School of Public Health Southwest Medical University Luzhou China. NetEase ThunderFire UX User Experience Center Netease Group Hangzhou China. School of International Relations and Public Affairs Fudan University Shanghai China. Center of Tissue Engineering and Stem Cell Research Guizhou Medical University Department of Pediatric Hematology The Affiliated Hospital of Guizhou Medical University Guiyang China. hezhixu2024@***. Clinical Research Center Affiliated Hospital of Southwest Medical University Luzhou China. jblei@***. School of Medical Information and Engineering Southwest Medical University Luzhou China. jblei@***. Center for Medical Informatics Institute of Advanced Clinical Medicine Peking University Beijing China. jblei@***.

digital therapeutics (DTx) provide innovative treatment options. In this study, we identified and compared approved DTx in China, the US, Germany, and Belgium. In total, 507 DTx applications were identified (US, 192; China, 235; Germany, 55; Belgium, 25). China has approved the most, whereas the US initiated approval as early as 2017. In China, most DTx applications focus on disease treatment (88.09%), particularly for neurological (45.96%) and ophthalmic (21.28%) diseases. The US and Belgium emphasize disease management (US: 52.60%, Belgium: 72.00%), whereas Germany focuses on the treatment of mental and behavioral disorders (47.27%). Regarding therapeutic approaches, China's DTx predominantly utilizes computerized cognitive correction (26.38%), whereas the US and Belgium primarily rely on health status monitoring (US, 44.27%; Belgium, 80.00%). Germany's DTx primarily employs cognitive behavioral therapy (69.09%). An online database was established to ensure ease of access. Future studies should focus on generating further evidence to support the broader adoption of DTx.

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A Model Based on RETE Algorithm for Generating Management and Control Measures for Key Personnel of Infectious Diseases

A Model Based on RETE Algorithm for Generating Management an...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Jinglong Chen Yao Hu Xueying Feng Feng Dai Yanquan Luan Qun Yuan Biao Du Research and Development Department Shandong Moses Network Technology Co. Ltd Yantai China Network Information Department Shandong Center for Disease Control and Prevention Jinan China Nutrition and Food Hygiene Section Yantai Center for Disease Control and Prevention Yantai China Operations Management Section Yantaishan Hospital Yantai China Digital Governance and Infrastructure Development Section Yantai Big Data Bureau Yantai China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

The prevention and control of infectious diseases require the determination of control measures for key personnel. However, in the past, manual research and judgement largely dictated the development of control measures. This approach was inefficient and resulted in a heavy workload, rendering standardization difficult. Furthermore, the traditional generation of control measures often proved unsustainable, curtailing progress towards utilizing an intelligent control system for key personnel. To alleviate the burden on frontline health workers and community disease control staff in implementing control measures, this thesis presents a model for generating intelligent control measures based on the RETE algorithm. Its purpose is to automate the control measures aimed at key individuals with infectious diseases. research has demonstrated that utilizing the algorithm model approach can enhance work efficiency by obtaining control measures that adhere to high standards and accuracy. Subsequently, frontline staff can be relieved from repetitive tasks and focus on ensuring the quality control of the generated control measures and resultant accuracy of the output.

关键词： Accuracy Infectious diseases Prevention and mitigation Quality control Manuals Rendering (computer graphics) Personnel Public healthcare Standards Intelligent control

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Poultrans: Application of Image Captioning in Diagnosing Avian Diseases and Exploring Their Characteristics

SSRN

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SSRN 2024年

作者： Li, Jun Yang, Bing Chen, Junyang Liu, Jiaxin Chen, Guanyu Zhang, Buyuan Amevor, Felix Kwame Zhao, Xiaoling College of Information Engineering Sichuan Agricultural University 46 Xinkang Road Yucheng District Sichuan Province Ya’an625000 China Agricultural Information Engineering Higher Institution Key Laboratory of Sichuan Province Sichuan Province Ya’an625000 China Ya’an Digital Agricultural Engineering Technology Research Center Sichuan Province Ya’an625000 China Mianyang City Commercial Bank Co. LTD. No. 3 Wenzhu Street Linyuan Road West Section Fucheng District Mianyang City621000 China Key Laboratory of Livestock and Poultry Multi-omics Colleg of Animal Science and Technology Sichuan Agricultural University Sichuan Chengdu China

Deep learning has made significant breakthrough in medical image recognition and analysis. It has been successfully used to accurately diagnosed several poultry diseases. Although current computational vision techniques have made significant achievements such as improving the accuracy, stability, and cross-platform compatibility of algorithms, however, they still fall short in providing users with an easy-to-understand basis for decision-making. Therefore, this study proposes the PoulTrans model - an image caption generation framework based on an advanced CNN-Transformer architecture that aims to enhance the transparency and credibility of the results obtained from diagnosing poultry diseases. The development of PDC-Captions dataset contributes a valuable resource for comprehensive study on generating descriptions of poultry diseases. The model incorporates the improved ResNext101 architecture and the Memory Transformer to promote memory enhancement, as well as to construct its encoder and decoder architecture. It is also equipped with an innovative PD-Loss augmented loss function, aiming to enhance the semantic accuracy of disease characterization. Furthermore, a new performance evaluation metric Weighted Mean Score (Sm) was introduced to provide a new scale for the multi-dimensional evaluation of model performance. In a comparative experiment, PoulTrans model achieved the highest scores of 0.3110, 0.8706, 5.0766, 0.6251 and 1.7209 in key performance indexes such as Blue4, ROUGEL, CIDEr, SPICE, and Sm, respectively. Compared with the optimal comparison model under the same experimental conditions, the improvements were 4.32%, 16.28%, 2.3271, 8.98%, and 66.93%, respectively. Through series of analyses such as visualization analysis, qualitative analysis, ablation experiment and generalization experiment, the validity and applicability of PoulTrans model were further confirmed. This study provides the basis for new research perspectives and tools used in automated di

关键词： Deep learning

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Development and application of MCAL tools for marine radar instruction

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Computer Applications in Engineering Education 1995年第4期3卷 259-259页

作者： Guirguis, S. Korany, E. Abdel-Bary, A. Institute of Graduate Studies and Research Alexandria University Alexandria Egypt Shawkat K. Guirguis:obtained the BSc and M.Sc. degrees in Computer Science and Automatic Control Faculty of Engineering Alexandria University in 1981 and 1984 respectively. In 1988 he obtained a PhD degree in Electronics and Communications Faculty of Engineering Cairo University co-supervised by the Imperial College of Science and Technology University of London where he spent two years as an academic visitor. His current research interests include software quality management multimedia automatic programming and decision support systems. He is currently a lecturer of Computer Science at the Institute of Graduate Studies & Research Alexandria University. Ezzat A. Korany:received the BS degree in Electrical Engineering (Electronics Section) from the University of Alexandria Egypt in 1971 the MS degree in Electrical Engineering (Computer Section) from Ain Shams University Cairo Egypt in 1977 and the PhD degree in Electrical Engineering (Digital Systems) from Florida Institute of Technology U.S.A. in 1982. He is currently an associate professor of Computer Science at the Institute of Graduate Studies & Research University of Alexandria. His research interests are in data communications and multimedia applications computer networks image processing and pattern recognition. Abdel-Latif A. Abdel-Bary:obtained the BEng degree in Electronics Technology from Arab Maritime Transport Academy in 1986. In 1995 he obtained the MSc degree in Information Technology from the Institute of Graduate Studies & Research Alexandria University. His current research interests include ICAL hypermedia and automatic programming.

In this article a multimedia computer-assisted learning (MCAL) system is presented. The major objective of this work was to investigate the potential of using such systems as tools for transferring instructional course information through various types of computer media as opposed to the classic CAL systems. The philosophy and techniques employed to design the system are investigated. Usage of the implemented system and its merits have been illustrated through its application to teach engineering students and technicians the theory and concepts of marine radar. System design, implementation, test, and revision phases are presented and discussed.

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Middle East and North African Health Informatics Association (MENAHIA): A New Opportunity for the Region

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Yearbook of Medical Informatics 2019年第1期28卷 292-296页

作者： Riyad Alshammari Najeeb Al-Shorbaji Zakiuddin Ahmed Zaid Al-Hamadan Abdulwahhab Alshammari Osama El Hassan Dari Alhuwail Salim Al Salmi Eiman Al-Jafar Hassan Ghazal College of Public Health and Health Informatics King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia King Abdullah International Medical Research Center Riyadh Saudi Arabia Ministry of the National Guard- Health Affairs Riyadh Saudi Arabia President of the Saudi Health Informatics Association President of the eHealth Development Association of Jordan Program Director RAHAH King Saud University Project Director Riphah Institute of Health Improvement & Safety CEO Digital Care Pakistan Associate Professor Jordan University of Science and Technology College of Applied Medical Sciences King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia Vice-Chair of Emirates Health Informatics Society Head of eHealth section at Dubai Health Authority Dubai United Arab Emirates Department of Information Science College of Computing Sciences and Engineering Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Associate Deanship for Health Information Management Program Oman College of Health Sciences Muscat Sultanate of Oman Department of Health Informatics and Information Management Faculty of Allied Health Sciences Kuwait University Kuwait. National Center for Scientific and Technological Research Rabat and President of the Moroccan Society for Telemedicine and eHealth Rabat Morocco

During the 2018 IMIA General Assembly (GA) held on Oct 2018 in Sri Lanka, a new organization, the Middle East and North African Health Informatics Association (MENAHIA) was established as a regional non-governmental organization to promote Health Informatics in the region. Health Informatics activities in applications, education, and research have been growing in the Middle East and North African region for many years. Over the past year, health informatics initiatives within the region focused on the education sector and the workforce. The need to enhance the education delivery and methods is essential in promoting Health Informatics in the region as well as establishing master programs in many countries within the region. This paper provides an overview of the variety of initiatives that are occurring in the region and are endorsed by MENAHIA.

关键词： Saudi Arabia Pakistan Jordan United Arab Emirates Morocco Kuwait Oman Bahrain Health Informatics the Middle East

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Assessing responsible innovation training

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Journal of Responsible technology 2023年 16卷

作者： Stahl, Bernd Carsten Aicardi, Christine Brooks, Laurence Craigon, Peter J. Cunden, Mayen Burton, Saheli Datta Heaver, Martin De Saille, Stevienna De Dolby, Serena Dowthwaite, Liz Eke, Damian Hughes, Stephen Keene, Paul Kuh, Vivienne Portillo, Virginia Shanley, Danielle Smallman, Melanie Smith, Michael Stilgoe, Jack Ulnicane, Inga Wagner, Christian Webb, Helena School of Computer Science University of Nottingham United Kingdom Dept. of Informatics and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Information School University of Sheffield Sheffield United Kingdom Horizon Digital Economy Research School of Computer Science University of Nottingham United Kingdom Independent Researcher Dept of Science and Technology Studies University College London United Kingdom ORBIT RRI limited Leicester United Kingdom Dept of Sociological Studies University of Sheffield Sheffield United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom School of Chemistry University of Bristol United Kingdom Department of Philosophy Faculty of Arts and Social Sciences Maastricht University Netherlands Biodiversity and Conservation Science Department of Biodiversity Conservation and Attractions Wildlife Research Centre Western Australia De Montfort University United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI. © 2023

关键词： Assessment Doctoral training Education Responsible innovation

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digital Fabrication technology in the Library: Where We Are and Where We Are Going

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Bulletin of the Association for information Science and technology 2016年第1期42卷

作者： Stephanie C. Prato public librarian Lauren Britton PhD candidate MLIS Syracuse University. She is a graduate of the School of Information Studies at Syracuse University (2012). She is committed to providing community members with tools opportunities and experiences that will enable them to be self-motivated lifelong learners. Stephanie is interested in creating dynamic hands-on learning opportunities in libraries especially around making STEM topics and digital creation. With experience in youth services technology and instruction she delights in helping people of all ages discover what the 21st century public library can do. Information science and technology at Syracuse University. Her research lies at the intersection of human computer interaction and social theory with particular interest in feminist science and technology studies and ubiquitous computing. Lauren's dissertation work explores the relationship between the human body and embedded/implanted technology. Lauren is a student researcher with the BITS Lab at Syracuse University.

EDITOR'S SUMMARY Makerspaces are environments where crafters and techies can come together to create, share and learn. Many libraries support the maker movement by providing access to a variety of technologies from sewing machines and looms to recording equipment and production tools, an expansion of services that is in line with promoting literacy, community and lifelong learning. About one third of makerspaces charged a fee or required membership in 2013, though some, including libraries and museums, are supported by major funding agencies. digital fabrication technology or 3D printers are increasingly available through library-based makerspaces and extend opportunities for STEM education, but libraries face challenges from high initial and ongoing costs, user training and even encouraging users in 3D thinking and design. 3D printers can be a powerful stimulus for creativity, learning and sharing, even promoting jobs and community development.

关键词： digital modeling and fabrication makerspaces 3D printers library and archival services access to resources diffusion of innovation lifelong learning creativity communities

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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