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arXiv 2018年

作者： Zhang, Hong Tong, Tiejun Landers, John Wu, Zheyang Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Department of Mathematics Hong Kong Baptist University Kowloon Tong Hong Kong Department of Neurology University of Massachusetts Medical School WorcesterMA01655 United States Department of Mathematical Sciences Program of Bioinformatics and Computational Biology Program of Data Science Worcester Polytechnic Institute WorcesterMA01609 United States

For testing a group of hypotheses, tremendous p-value combination methods have been developed and widely applied since 1930's. Some methods (e.g., the minimal p-value) are optimal for sparse signals, and some others (e.g., Fisher's combination) are optimal for dense signals. To address a wide spectrum of signal patterns, this paper proposes a unifying family of statistics, called TFisher, with general p-value truncation and weighting schemes. Analytical calculations for the p-value and the statistical power of TFisher under general hypotheses are given. Optimal truncation and weighting parameters are studied based on Bahadur Efficiency (BE) and the proposed Asymptotic Power Efficiency (APE), which is superior to BE for studying the signal detection problem. A soft-thresholding scheme is shown to be optimal for signal detection in a large space of signal patterns. When prior information of signal pattern is unavailable, an omnibus test, oTFisher, can adapt to the given data. Simulations evidenced the accuracy of calculations and validated the theoretical properties. The TFisher tests were applied to analyzing a whole exome sequencing data of amyotrophic lateral sclerosis. Relevant tests and calculations have been implemented into an R package TFisher and published on the CRAN. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal detection

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Multiple testing with persistent homology

arXiv

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arXiv 2018年

作者： Vejdemo-Johansson, Mikael Mukherjee, Sayan CUNY College of Staten Island CUNY Graduate Center 2800 Victory Boulevard 1S-215 Staten Island NY10314 United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Humboldtstraße 25 Leipzig04105 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics Duke University DurhamNC27708 United States

In this paper we propose a computationally efficient multiple hypothesis testing procedure for persistent homology. The computational efficiency of our procedure is based on the observation that one can empirically simulate a null distribution that is universal across many hypothesis testing applications involving persistence homology. Our observation suggests that one can simulate the null distribution efficiently based on a small number of summaries of the collected data and use this null in the same way that p-value tables were used in classical statistics. To illustrate the efficiency and utility of the null distribution we provide procedures for rejecting acyclicity with both control of the Family-Wise Error Rate (FWER) and the False Discovery Rate (FDR). We will argue that the empirical null we propose is very general conditional on a few summaries of the data based on simulations and limit theorems for persistent homology for point *** Codes 55N31 Copyright © 2018, The Authors. All rights reserved.

关键词： Computational efficiency

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Correction to: Risk factors for mortality in hospitalized patients with COVID-19 at the start of the pandemic in Belgium: a etrospective cohort study

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BMC infectious diseases 2020年第1期20卷 956页

作者： Karlijn van Halem Robin Bruyndonckx Jeroen van der Hilst Janneke Cox Paulien Driesen Matthias Opsomer Eveline Van Steenkiste Björn Stessel Jasperina Dubois Peter Messiaen Department of Infectious Diseases and Immunity Jessa Hospital Stadsomvaart 11 3500 Hasselt Belgium. karlijn.vanhalem@jessazh.be. Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BIOSTAT) Data Science Institute (DSI) Hasselt University Hasselt Belgium. Laboratory of Medical Microbiology Vaccine & Infectious Disease Institute (VAXINFECTIO) University of Antwerp Universiteitsplein 1 2610 Antwerp Belgium. Department of Infectious Diseases and Immunity Jessa Hospital Stadsomvaart 11 3500 Hasselt Belgium. Faculty of Medicine and Health Sciences Hasselt University Hasselt Belgium. Department of Intensive Care Jessa Hospital Hasselt Belgium.

An amendment to this paper has been published and can be accessed via the original article.

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Distributions and statistical power of optimal signal-detection methods in finite cases

arXiv

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arXiv 2017年

作者： Zhang, Hong Jin, Jiashun Wu, Zheyang Worcester Polytechnic Institute Carnegie Mellon University Department of Mathematical Sciences Program of Bioinformatics and Computational Biology Program of Data Science 100 Institute Road Worcester01609 Morocco

In big data analysis for detecting rare and weak signals among n features, some grouping-test methods such as Higher Criticism test (HC), Berk-Jones test (B-J), and φ-divergence test share the similar asymptotical optimality when n → ∞. However, in practical data analysis n is frequently small and moderately large at most. In order to properly apply these optimal tests and wisely choose them for practical studies, it is important to know how to get the p-values and statistical power of them. To address this problem in an even broader context, this paper provides analytical solutions for a general family of goodness-of-fit (GOF) tests, which covers these optimal tests. For any given i.i.d. and continuous distributions of the input test statistics of the n features, both p-value and statistical power of such a GOF test can be calculated. By calculation we compared the finite-sample performances of asymptotically optimal tests under the normal mixture alternative. Results show that HC is the best choice when signals are rare, while B-J is more robust over various signal patterns. In the application to a real genome-wide association study, results illustrate that the p-value calculation works well, and the optimal tests have potentials for detecting novel disease genes with weak genetic effects. The calculations have been implemented in an R package SetTest and published on the CRAN. Copyright © 2017, The Authors. All rights reserved.

关键词： Information analysis

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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Identification of targetable vulnerabilities of PLK1-overexpressing cancers by synthetic dosage lethality

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Cell Genomics 2025年第6期5卷 100876页

作者： Cunningham, Chelsea E. Vizeacoumar, Frederick S. Zhang, Yue Kyrylenko, Liliia Both, Simon Maranda, Vincent Dong, He Price, Jared D.W. Gao, Peng Wagner, Konrad Wu, Yingwen Lazell-Wright, Mary Ganapathysamy, Ashtalakshmi Hari, Rithik Bhanumathy, Kalpana K. Denomy, Connor Saxena, Anjali Vizeacoumar, Jeff P. Morales, Alain Morejon Khan, Faizaan Mosley, Shayla Chen, Angie Katrii, Tetiana Zoller, Ben G.E. Rajamanickam, Karthic Walke, Prachi Gong, Lihui Patel, Hardikkumar Elhasasna, Hussain Dahiya, Renuka Abuhussein, Omar Dmitriev, Anton Freywald, Tanya Munhoz, Erika Prando Ruppin, Eytan Lee, Joo Sang Rox, Katharina Koebel, Martin Hopkins, Laura Lee, Cheng Han Yadav, Sunil Gasparoni, Gilles Walter, Jörn Krishnan, Anand Datla, Raju Toosi, Behzad Baker, Kristi Meens, Jalna Cescon, David W. Ailles, Laurie Leary, Scot C. Wu, Yuliang Empting, Martin Kiemer, Alexandra K. Freywald, Andrew Vizeacoumar, Franco J. Department of Oncology College of Medicine University of Saskatchewan Saskatoon S7N 5E5 SK Canada Department of Pathology and Laboratory Medicine University of Saskatchewan Saskatoon S7N 5E5 SK Canada Department of Pharmacy Pharmaceutical Biology Saarland University PharmaScienceHub Saarbrücken 66123 Germany Global Institute for Food Security University of Saskatchewan Saskatoon S7N 4L8 SK Canada Agriculture and Agri-Food Canada Saskatoon Research and Development Centre 107 Science Place Saskatoon S7N 0X2 SK Canada Antiviral & Antivirulence Drugs (AVID) Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) Helmholtz Centre for Infection Research (HZI) and Department of Pharmacy Saarland University Saarbrücken 66123 Germany Department of Oncology University of Alberta Edmonton T6G 1Z2 AB Canada Department of Anatomy Physiology and Pharmacology University of Saskatchewan and Cameco MS Neuroscience Research Centre 701 Queen St. Saskatoon S7K 0M7 SK Canada Cancer Data Science Laboratory National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Center for Bioinformatics and Computational Biology and Department of Computer Sciences University of Maryland College Park 20742 MD United States Department of Precision Medicine School of Medicine and Department of Artificial Intelligence Sungkyunkwan University Suwon 16419 South Korea Department of Chemical Biology (CBIO) Helmholtz Center for Infection Research (HZI) Inhoffenstrasse 7 Braunschweig 38124 Germany Department of Pathology University of Calgary Calgary AB Canada Department of Laboratory Medicine and Pathology University of Alberta Edmonton AB Canada Department of Genetics Saarland University Saarbrücken Germany Western College of Veterinary Medicine University of Saskatchewan Room 2343 52 Campus Drive Saskatoon S7N 5B4 Canada Princess Margaret Cancer Centre University Health Network Toronto ON Canada Department of Biochemistry Microb

Chromosomal instability (CIN) drives tumor heterogeneity, complicating cancer therapy. Although Polo-like kinase 1 (PLK1) overexpression induces CIN, direct inhibition of PLK1 has shown limited clinical benefits. We therefore performed a genome-wide synthetic dosage lethality (SDL) screen to identify effective alternative targets and validated over 100 candidates using in vivo and in vitro secondary CRISPR screens. We employed direct-capture Perturb-seq to assess the transcriptional consequences and viability of each SDL perturbation at a single-cell resolution. This revealed IGF2BP2 as a critical genetic dependency that, when targeted, downregulated PLK1 and significantly restricted tumor growth. Mechanistic analyses showed that IGF2BP2 loss disrupted cellular energy metabolism and mitochondrial ATP production by downregulating PLK1 levels as well as genes associated with oxidative phosphorylation. Consistent with this, pharmacological inhibition of IGF2BP2 severely impacts the viability of PLK1-overexpressing cancer cells addicted to higher metabolic rates. Our work offers a novel therapeutic strategy against PLK1-driven heterogeneous malignancies. © 2025 The Author(s)

关键词： chromosomal instability IGF2BP2 IMP2 in vivo CRISPR screen perturb-seq PLK1 single-cell CRISPR screening synthetic dosage lethality tumor heterogeneity

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A Direct Proof of Improved Biased Random Walk with Gastric Cancer dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

引用

International Conference on Applied Mathematics & Computer science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

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STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence reliability

iMetaOmics

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iMetaOmics 2024年第1期1卷

作者： Jinghong Chen Lingxuan Zhu Weiming Mou Anqi Lin Dongqiang Zeng Chang Qi Zaoqu Liu Aimin Jiang Bufu Tang Wenjie Shi Ulf D. Kahlert Jianguo Zhou Shipeng Guo Xiaofan Lu Xu Sun Trunghieu Ngo Zhongji Pu Baolei Jia Che Ok Jeon Yongbin He Haiyang Wu Shuqin Gu Wisit Cheungpasitporn Haojie Huang Weipu Mao Shixiang Wang Xin Chen Loïc Cabannes Gerald Sng Gui Ren Iain S. Whitaker Stephen Ali Quan Cheng Kai Miao Shuofeng Yuan Peng Luo Department of Oncology Zhujiang Hospital Southern Medical University Guangzhou China The Second School of Clinical Medicine Southern Medical University Guangzhou China Department of Urology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Oncology Nanfang Hospital Southern Medical University Guangzhou China Institute of Logic and Computation TU Wien Wien Austria Institute of Basic Medical Sciences Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Department of Urology Changhai Hospital Naval Medical University (Second Military Medical University) Shanghai China Department of Radiation Oncology Zhongshan Hospital Fudan University Shanghai China Molecular and Experimental Surgery University Clinic for General- Visceral- Vascular- and Trans-Plantation Surgery Medical Faculty University Hospital Magdeburg Otto-von Guericke University Magdeburg Germany Department of Oncology The Second Affiliated Hospital of Zunyi Medical University Zunyi China Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Comprehensive Cancer Center Erlangen-EMN Erlangen Germany GZDLab Chongqing China Department of Cancer and Functional Genomics Institute of Genetics and Molecular and Cellular Biology CNRS/INSERM/UNISTRA Illkirch France Linguistique Informatique UFR-Linguistique Université Paris Cité Paris France Xianghu Laboratory Hangzhou China Department of Life Science Chung-Ang University Seoul Korea School of Sport Medicine and Rehabilitation Beijing Sport University Beijing China Department of Microbiology and Immunology School of Medicine Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Department of Graduate School Tianjin Medical University Tianjin China Department of Clinical College of Neurology Neurosurgery and Neurorehabilitation Tianjin Medical Universi

Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32-item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit *** . This work formulates the standardized testing and assessment guidelines for evaluating generative artificial intelligence (AI) reliability (STAGER) checklist, a 32-item framework offering standardized assessment guidelines tailored for evaluating generative AI systems in medical and life science contexts. It consists of key aspects, including question collection, querying approaches, and assessment techniques. It enhances research quality and facilitates advances in

关键词： generative AI medical and life science contexts reliability standardized assessment guidelines

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Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

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Intelligence-Based Medicine 2022年 6卷 100071-100071页

作者： Smit, J.M. Krijthe, J.H. Endeman, H. Tintu, A.N. de Rijke, Y.B. Gommers, D.A.M.P.J. Cremer, O.L. Bosman, R.J. Rigter, S. Wils, E.-J. Frenzel, T. Dongelmans, D.A. De Jong, R. Peters, M.A.A. Kamps, M.J.A. Ramnarain, D. Nowitzky, R. Nooteboom, F.G.C.A. De Ruijter, W. Urlings-Strop, L.C. Smit, E.G.M. Mehagnoul-Schipper, D.J. Dormans, T. De Jager, C.P.C. Hendriks, S.H.A. Achterberg, S. Oostdijk, E. Reidinga, A.C. Festen-Spanjer, B. Brunnekreef, G.B. Cornet, A.D. Van den Tempel, W. Boelens, A.D. Koetsier, P. Lens, J.A. Faber, H.J. karakus, A. Entjes, R. De Jong, P. Rettig, T.C.D. Arbous, M.S. Lalisang, R.C.A. Tonutti, M. De Bruin, D.P. Elbers, P.W.G. Van Bommel, J. Reinders, M.J.T. Intensive Care Erasmus University Medical Center Rotterdam Netherlands Pattern Recognition & Bioinformatics Group EEMCS Delft University of Technology Delft Netherlands Clinical Chemistry Erasmus University Medical Center Rotterdam Netherlands Intensive Care UMC Utrecht Utrecht Netherlands Intensive Care OLVG Amsterdam Netherlands Anesthesiology and Intensive Care St. Antonius Hospital Nieuwegein Netherlands Intensive Care Franciscus Gasthuis Vlietland Rotterdam Netherlands Intensive Care Radboud University Medical Center Nijmegen Netherlands Intensive Care Amsterdam UMC Amsterdam Netherlands Intensive Care Bovenij Ziekenhuis Amsterdam Netherlands Intensive Care Canisius Wilhelmina Ziekenhuis Nijmegen Netherlands Intensive Care Catharina Ziekenhuis Eindhoven Eindhoven Netherlands Intensive Care ETZ Tilburg Tilburg Netherlands Intensive Care HagaZiekenhuis Den Haag Netherlands Intensive Care Laurentius Ziekenhuis Roermond Netherlands Intensive Care Northwest Clinics Alkmaar Netherlands Intensive Care Reinier de Graaf Gasthuis Delft Netherlands Intensive Care Spaarne Gasthuis Haarlem en Hoofddorp Netherlands Intensive Care VieCuri Medisch Centrum Venlo Netherlands Intensive Care Zuyderland MC Heerlen Netherlands Intensive Care Jeroen Bosch Ziekenhuis 's-Hertogenbosch Netherlands Intensive Care Albert Schweitzerziekenhuis Dordrecht Netherlands Intensive Care Haaglanden Medisch Centrum Den Haag Netherlands Intensive Care Maasstad Ziekenhuis Rotterdam Rotterdam Netherlands Intensive Care SEH BWC Martiniziekenhuis Groningen Netherlands Intensive Care Ziekenhuis Gelderse Vallei Ede Netherlands Intensive Care Ziekenhuisgroep Twente Almelo Netherlands Intensive Care Medisch Spectrum Twente Enschede Netherlands Intensive Care Ikazia Ziekenhuis Rotterdam Rotterdam Netherlands Anesthesiology Antonius Ziekenhuis Sneek Sneek Netherlands Intensive Care Medisch Centrum Leeuwarden Leeuwarden Netherlands Intensive Care IJsselland Zieke

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85;0.88] and 0.86 [0.84;0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12;0.04] and slope of 0.90 [0.85;0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27;−0.10] and slope of 0.89 [0.84;0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research. © 2022 The Authors

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Nanopublications: A Growing Resource of Provenance-Centric Scientific Linked data

Nanopublications: A Growing Resource of Provenance-Centric S...

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IEEE International Conference on e-science and Grid Computing

作者： Tobias Kuhn Albert Meroño-Peñuela Alexander Malic Jorrit H. Poelen Allen H. Hurlbert Emilio Centeno Ortiz Laura I. Furlong Núria Queralt-Rosinach Christine Chichester Juan M. Banda Egon Willighagen Friederike Ehrhart Chris Evelo Tareq B. Malas Michel Dumontier Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands Vrije Universiteit Amsterdam Amsterdam Noord-Holland NL Institute of Data Science Maastricht University Netherlands 400 Perkins St Oakland California USA Department of Biology University of North Carolina Chapel Hill USA Research Programme on Biomedical Informatics Universitat Pompeu Fabra Barcelona Spain Department of Integrative Structural and Computational Biology The Scripps Research Institute La Jolla USA Datafair.xyz Geneva Switzerland Department of Computer Science Georgia State University USA Department of Bioinformatics BiGCaT NUTRIM Maastricht University The Netherlands Department of Human Genetics Leiden University Medical Center Netherlands

Nanopublications are a Linked data format for scholarly data publishing that has received considerable uptake in the last few years. In contrast to the common Linked data publishing practice, nanopublications work at the granular level of atomic information snippets and provide a consistent container format to attach provenance and metadata at this atomic level. While the nanopublications format is domain-independent, the datasets that have become available in this format are mostly from Life science domains, including data about diseases, genes, proteins, drugs, biological pathways, and biotic interactions. More than 10 million such nanopublications have been published, which now form a valuable resource for studies on the domain level of the given Life science domains as well as on the more technical levels of provenance modeling and heterogeneous Linked data. We provide here an overview of this combined nanopublication dataset, show the results of some overarching analyses, and describe how it can be accessed and queried.

关键词： Nanobioscience Diseases Linked data Proteins bioinformatics Publishing databases

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