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Rare genetic variation in PTPRB is associated with central serous chorioretinopathy, varicose veins and glaucoma

Nature Communications 2025年第1期16卷 1-13页

作者： Rämö, Joel T. Gorman, Bryan R. Weng, Lu-Chen Jurgens, Sean J. Singhanetr, Panisa Tieger, Marisa G. van Dijk, Elon HC Halladay, Christopher W. Wang, Xin Hauser, Blake M. Kim, Soo Hyun Brinks, Joost Choi, Seung Hoan Luo, Yuyang Pyarajan, Saiju Nealon, Cari L. Gorin, Michael B. Wu, Wen-Chih Anthony, Scott A. Roncone, David P. Sobrin, Lucia Kaarniranta, Kai Yzer, Suzanne Palotie, Aarno Peachey, Neal S. Turunen, Joni A. Boon, Camiel JF Ellinor, Patrick T. Iyengar, Sudha K. Daly, Mark J. Rossin, Elizabeth J. Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Massachusetts Eye and Ear Boston MA United States Cardiovascular Disease Initiative Broad Institute of MIT and Harvard Cambridge MA United States Cardiovascular Research Center Massachusetts General Hospital Boston MA United States Center for Data and Computational Sciences (C-DACS) VA Cooperative Studies Program VA Boston Healthcare System Boston MA United States Booz Allen Hamilton McLean VA United States Department of Experimental Cardiology Amsterdam Cardiovascular Sciences Heart Failure & Arrhythmias Amsterdam UMC University of Amsterdam Amsterdam Netherlands Mettapracharak Eye Institute Mettapracharak (Wat Rai Khing) Hospital Nakhon Pathom Thailand New England Eye Center Tufts Medical Center Boston MA United States Department of Ophthalmology Leiden University Medical Center Leiden Netherlands Center of Innovation in Long Term Services and Supports Providence VA Medical Center Providence RI United States Harvard Medical School Boston MA United States Department of Biostatistics Boston University Boston MA United States VA Cooperative Studies Program VA Boston Healthcare System Boston MA United States Department of Medicine Brigham and Women’s Hospital and Harvard School of Medicine Boston MA United States Eye Clinic VA Northeast Ohio Healthcare System Cleveland OH United States Department of Ophthalmology David Geffen School of Medicine Stein Eye Institute University of California Los Angeles Los Angeles CA United States Department of Human Genetics David Geffen School of Medicine Stein Eye Institute University of California Los Angeles Los Angeles CA United States Section of Cardiology Medical Service VA Providence Healthcare System Providence RI United States Harvard Medical School Department of Ophthalmology Massachusetts Eye and Ear Boston MA United States Department of Ophthalmology Unive

Central serous chorioretinopathy is an eye disease characterized by fluid buildup under the central retina whose etiology is not well understood. Abnormal choroidal veins in central serous chorioretinopathy patients have been shown to have similarities with varicose veins. To identify potential mechanisms, we analyzed genotype data from 1,477 patients and 455,449 controls in FinnGen. We identified an association for a low-frequency (allele frequency = 0.5%) missense variant (rs113791087) in PTPRB, the gene encoding vascular endothelial protein tyrosine phosphatase (odds ratio=2.85, P = 4.5 × 10^-9). This was confirmed in a meta-analysis of 2,452 patients and 865,767 controls from 4 studies (odds ratio=3.06, P = 7.4 × 10^-15). Rs113791087 was associated with a 56% higher prevalence of retinal abnormalities (35.3% vs 22.6%, P = 8.0 × 10^-4) in 708 UK Biobank participants and, surprisingly, with increased risk of varicose veins (odds ratio=1.31, P = 2.3 × 10^-11) and reduced risk of glaucoma (odds ratio=0.82, P = 6.9 × 10^-9). Predicted loss-of-function variants in PTPRB, though rare in number, were associated with central serous chorioretinopathy in All of Us (odds ratio=17.09, P = 0.018). These findings highlight the significance of vascular endothelial protein tyrosine phosphatase in diverse ocular and systemic veno-vascular diseases. © The Author(s) 2025.

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Differential Receptor Tyrosine Kinase Phosphorylation in the Uterus of Rats Following Developmental Exposure to Tetrabromobisphenol A

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Toxicology research and application 2021年第5期5卷

作者： Lysandra Castro Jingli Liu Linda Yu Alanna D Burwell Trey O Saddler Lindsay A Santiago William Xue Julie F Foley Michael Staup Norris D Flagler Min Shi Linda S Birnbaum Dixon Darlene Mechanistic Toxicology Branch Division of the National Toxicology Program (DNTP) National Institute of Environmental Health Sciences (NIEHS) 111 TW Alexander Drive Research Triangle Park NC 27709 USA. Office of Data Science DNTP NIEHS 111 TW Alexander Drive Research Triangle Park NC 27709 USA. Charles River Laboratories Inc. Durham NC 27703 USA. Cellular and Molecular Pathogenesis Branch DNTP NIEHS 111 TW Alexander Drive Research Triangle Park NC 27709 USA. Biostatistics and Computational Biology Branch Division of the Intramural Program National Institute of Environmental Health Sciences (NIEHS) Research Triangle Park NC 27709 USA.

Tetrabromobisphenol A (TBBPA) is a brominated flame retardant that induces endometrial adenocarcinoma and other uterine tumors in Wistar Han rats; however, early molecular events or biomarkers of TBBPA exposure remain unknown. We investigated the effects of TBBPA on growth factor receptor activation (phospho-RTK) in uteri of rats following early-life exposures. Pregnant Wistar Han rats were exposed to TBBPA (0, 0.1, 25, 250 mg/kg/day) via oral gavage on gestation day 6 through weaning of pups (PND 21). Pups were exposed through lactation, and by daily gavage from PND 22 to PND 90. Uterine horns were collected (at PND 21, PND 33, PND 90) and formalin-fixed or frozen for histologic, immunohistochemical, phospho-RTK arrays, or western blot analysis. At PND 21, the phosphor-RTKs, FGFR2, FGFR3, TRKC and EPHA1 were significantly increased at different treatment concentrations. Several phospho-RTKs were also significantly overexpressed at PND 33 which included epithelial growth factor receptor (EGFR), Fibroblast Growth Factor Receptor 3-4 (FGFR2, FGFR3, FGFR4), insulin-like growth factor receptor 1 (IGF1R), INSR, AXL, MERTK, PDGFRa and b, RET, Tyrosine Kinase with Immunoglobulin Like and EGF Like Domains 1 and 2 (TIE1; TIE2), TRKA, VEGFR2 and 3, and EPHA1 at different dose treatments. EGFR, an RTK overexpressed in endometrial cancer in women, remained significantly increased for all treatment groups at PND 90. Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2) and IGF1R were overexpressed at PND 33 and remained increased through PND 90, although ERBB2 was statistically significant at PND 90. The phospho-RTKs, FGFR3, AXL, DTK, HGFR, TRKC, VEGFR1 and EPHB2 and 4 were also statistically significant at PND 90 at different dose treatments. The downstream effector, phospho-MAPK44/42 was also increased in uteri of treated rats. Our findings show RTKs are dysregulated following early life TBBPA exposures and their sustained activation may contribute to TBBPA-induced uterine tumors observe

关键词： Endometrial Adenocarcinoma Growth Factor Receptors Tetrabromobisphenol A (TBBPA) Uterus Wistar Han Rats

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26th Annual computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第Sup1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Kings County Hospital Center Brooklyn NY 11203 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics

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Neuroscience needs Network science

arXiv

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arXiv 2023年

作者： Barabási, Dániel L. Bianconi, Ginestra Bullmore, Ed Burgess, Mark Chung, SueYeon Eliassi-Rad, Tina George, Dileep Kovács, István A. Makse, Hernán Papadimitriou, Christos Nichols, Thomas E. Sporns, Olaf Stachenfeld, Kim Toroczkai, Zoltán Towlson, Emma K. Zador, Anthony M. Zeng, Hongkui Barabási, Albert-László Bernard, Amy Buzsáki, György Biophysics Program Harvard University CambridgeMA United States Department of Molecular and Cellular Biology Center for Brain Science Harvard University CambridgeMA United States School of Mathematical Sciences Queen Mary University of London LondonE1 4NS United Kingdom The Alan Turing Institute The British Library LondonNW1 2DB United Kingdom Department of Psychiatry Wolfson Brain Imaging Centre University of Cambridge Cambridge United Kingdom ChiTek-i AS Oslo Norway Center for Neural Science New York University New YorkNY United States Center for Computational Neuroscience Flatiron Institute Simons Foundation New YorkNY United States Network Science Institute Northeastern University BostonMA United States Khoury College of Computer Sciences Northeastern University BostonMA United States Santa Fe Institute Santa FeNM United States DeepMind United Kingdom Department of Physics and Astronomy Northwestern University 633 Clark Street EvanstonIL60208 United States Northwestern Institute on Complex Systems Chambers Hall Northwestern University 600 Foster St EvanstonIL60208 United States Levich Institute and Physics Department City College of New York New YorkNY10031 United States Columbia University New YorkNY10027 United States Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Population Health University of Oxford OxfordOX3 7LF United Kingdom Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford OxfordOX3 9DU United Kingdom Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States Department of Physics University of Notre Dame 225 Nieuwland Science Hall Notre DameIN46556 United States Department of Computer Science Department of Physics and Astronomy Hotchkiss Brain Institute Children’s Research Hospital University of Calgary CalgaryAB Canada Cold Spring Harbor Laboratory

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions. © 2023, CC BY-NC-SA.

关键词： Complex networks

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Correction: Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers

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BMC Digital Health 2023年第1期1卷 1-1页

作者： Thomas Wetere Tulu Ching Long Chan Chun Hei Wu Kei Hang Katie Chan Tsz Kin Wan Peter Yat Ming Woo Cee Zhung Steven Tseng Asmir Vodencarevic Cristina Menni Department of Biomedical Sciences City University of Hong Kong Hong Kong SAR China Computational Data Science Program Addis Ababa University Addis Ababa Ethiopia Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China Department of Epidemiology and Center for Global Cardiometabolic Health School of Public Health Brown University Providence RI USA Department of Neurosurgery Kwong Wah Hospital Hong Kong SAR China Department of Medicine and Geriatrics Kwong Wah Hospital Hong Kong SAR China Innovative Medicines Novartis Pharma GmbH 90429 Nuremberg Germany Department of Twin Research King’s College London London UK

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Classification of datasets with imputed missing values: does imputation quality matter?

arXiv

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arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Ontologizing Health Systems data at Scale: Making Translational Discovery a Reality

arXiv

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arXiv 2022年

作者： Callahan, Tiffany J. Stefanski, Adrianne L. Wyrwa, Jordan M. Zeng, Chenjie Ostropolets, Anna Banda, Juan M. Baumgartner, William A. Boyce, Richard D. Casiraghi, Elena Coleman, Ben D. Collins, Janine H. Deakyne-Davies, Sara J. Feinstein, James A. Haendel, Melissa A. Lin, Asiyah Y. Martin, Blake Matentzoglu, Nicolas A. Meeker, Daniella Reese, Justin Sinclair, Jessica Taneja, Sanya B. Trinkley, Katy E. Vasilevsky, Nicole A. Williams, Andrew Zhang, Xingman A. Denny, Joshua C. Robinson, Peter N. Ryan, Patrick Hripcsak, George Bennett, Tellen D. Hunter, Lawrence E. Kahn, Michael G. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States Department of Computer Science Georgia State University AtlantaGA30303 United States Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15260 United States Computer Science Università degli Studi di Milano Milan20122 Italy Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States The Jackson Laboratory for Genomic Medicine FarmingtonCT06032 United States Department of Haematology University of Cambridge Cambridge United Kingdom Department of Research Informatics & Data Science Analytics Resource Center Children’s Hospital Colorado AuroraCO80045 United States University of Colorado Anschutz School of Medicine AuroraCO80045 United States Center for Health AI University of Colorado Anschutz Medical Campus AuroraCO80045 United States Departments of Biomedical Informatics and Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Semanticly Athens Greece Yale School of Medicine New HavenCT06510 United States HealthLinc ValparaisoIN46383 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Family Medicine University of Colorado Anschutz School of Medicine AuroraCO80045 United States Translational and Integrative Sciences Lab University of Colorado Anschutz Medical Campus AuroraCO80045 United States Tufts Institute for Clinical Research and Health Policy Studies Tufts University Bos

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping. © 2022, CC BY.

关键词： Ontology

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Author Correction: A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing

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Nature communications 2022年第1期13卷 2828页

作者： Sanju Sinha Karina Barbosa Kuoyuan Cheng Mark D M Leiserson Prashant Jain Anagha Deshpande David M Wilson Bríd M Ryan Ji Luo Ze'ev A Ronai Joo Sang Lee Aniruddha J Deshpande Eytan Ruppin Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20892 USA. Laboratory of Human Carcinogenesis Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20850 USA. Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 USA. Tumor Initiation Program Cancer Center Sanford Burnham Prebys Medical Discovery Institute La Jolla CA 92037 USA. Laboratory of Molecular Gerontology National Institute on Aging Intramural Research Program National Institutes of Health Baltimore MD 21224 USA. Laboratory of Cancer Biology and Genetics Center for Cancer Research National Cancer Institute National Institute of Health Bethesda MD 20850 USA. Samsung Medical Center Sungkyunkwan University School of Medicine Suwon 16419 Republic of Korea. Department of Precision Medicine School of Medicine and Department of Artificial Intelligence Sungkyunkwan University Suwon 16419 Republic of Korea. Tumor Initiation Program Cancer Center Sanford Burnham Prebys Medical Discovery Institute La Jolla CA 92037 USA. adeshpande@***. Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20892 USA. eytan.ruppin@nih.gov.

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