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arXiv 2024年

作者： Zeiltinger, Julia Roy, Sushmita Ay, Ferhat Mathelier, Anthony Medina-Rivera, Alejandra Mahony, Shaun Sinha, Saurabh Ernst, Jason Stowers Institute for Medical Research Kansas CityMO64112 United States Department of Pathology & Laboratory Medicine The University of Kansas Medical Center Kansas CityKS66160 United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison Madison53715 United States Wisconsin Institute for Discovery University of Wisconsin-Madison Madison53715 United States Centers for Autoimmunity Inflammation and Cancer Immunotherapy La Jolla Institute for Immunology La Jolla CA United States Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA United States Department of Pediatrics University of California La Jolla San DiegoCA United States Nordic EMBL Partnership University of Oslo Oslo0318 Norway Department of Medical Genetics Institute of Clinical Medicine University of Oslo Oslo University Hospital Oslo Norway Department of Pharmacy University of Oslo Oslo Norway Laboratorio Internacional de Investigación sobre el Genoma Humano Universidad Nacional Autónoma de México Campus Juriquilla Blvd. Juriquilla 3001 Santiago de Querétaro76230 Mexico Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA16802 United States Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Department of Biological Chemistry University of California Los Angeles Los AngelesCA90095 United States Computer Science Department University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Eli and Edythe Broad Center of Regenerative Medicine Stem Cell Research University of California Los Angeles Los AngelesCA90095 United States

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals. © 2024, CC BY-NC-SA.

关键词： Molecular docking

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Author Correction: Mining for encrypted peptide antibiotics in the human proteome

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Nature biomedical engineering 2022年 2022 Dec 1页

作者： Marcelo D T Torres Marcelo C R Melo Laurice Flowers Orlando Crescenzi Eugenio Notomista Cesar de la Fuente-Nunez Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania Philadelphia PA USA. Penn Institute for Computational Science University of Pennsylvania Philadelphia PA USA. Departments of Dermatology and Microbiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Chemical Sciences University of Naples Federico II Naples Italy. Department of Biology University of Naples Federico II Naples Italy. Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. cfuente@upenn.edu. Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania Philadelphia PA USA. cfuente@upenn.edu. Penn Institute for Computational Science University of Pennsylvania Philadelphia PA USA. cfuente@upenn.edu.

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OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle

arXiv

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arXiv 2020年

作者： Yu, Haijun Tian, Xinyuan Weinan, E. Li, Qianxiao NCMIS LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics The Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics National University of Singapore Singapore119077 Singapore Institute of High Performance Computing A*STAR Singapore138632 Singapore

We propose a systematic method for learning stable and physically interpretable dynamical models using sampled trajectory data from physical processes based on a generalized Onsager principle. The learned dynamics are autonomous ordinary differential equations parameterized by neural networks that retain clear physical structure information, such as free energy, diffusion, conservative motion and external forces. For high dimensional problems with a low dimensional slow manifold, an autoencoder with metric preserving regularization is introduced to find the low dimensional generalized coordinates on which we learn the generalized Onsager dynamics. Our method exhibits clear advantages over existing methods on benchmark problems for learning ordinary differential equations. We further apply this method to study Rayleigh-Bénard convection and learn Lorenz-like low dimensional autonomous reduced order models that capture both qualitative and quantitative properties of the underlying dynamics. This forms a general approach to building reduced order models for forced dissipative *** Codes 76E30, 34D20, 68T05/07, 82C35 Copyright © 2020, The Authors. All rights reserved.

关键词： Ordinary differential equations

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GREGoR: Accelerating Genomics for Rare Diseases

arXiv

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arXiv 2024年

作者： Dawood, Moez Heavner, Ben Wheeler, Marsha M. Ungar, Rachel A. LoTempio, Jonathan Wiel, Laurens Berger, Seth Bernstein, Jonathan A. Chong, Jessica X. Délot, Emmanuèle C. Eichler, Evan E. Gibbs, Richard A. Lupski, James R. Shojaie, Ali Talkowski, Michael E. Wagner, Alex H. Wei, Chia-Lin Wellington, Christopher Wheeler, Matthew T. Carvalho, Claudia M.B. Gifford, Casey A. May, Susanne Miller, Danny E. Rehm, Heidi L. Sedlazeck, Fritz J. Vilain, Eric O'Donnell-Luria, Anne Posey, Jennifer E. Chadwick, Lisa H. Bamshad, Michael J. Montgomery, Stephen B. Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX United States Medical Scientist Training Program Baylor College of Medicine HoustonTX United States Department of Biostatistics University of Washington SeattleWA United States Department of Genetics School of Medicine Stanford University StanfordCA United States Department of Pathology School of Medicine Stanford University Stanford CA United States Stanford Center for Biomedical Ethics School of Medicine Stanford University Stanford CA United States Institute for Clinical and Translational Science University of California IrvineCA United States Division of Cardiovascular Medicine School of Medicine Stanford University Stanford CA United States Division of Genetics and Metabolism Children’s National Rare Disease Institute WashingtonDC United States Center for Genetic Medicine Research Children’s National Rare Disease Institute WashingtonDC United States Department of Genomics and Precision Medicine George Washington University WashingtonDC United States Department of Pediatrics School of Medicine Stanford University Stanford CA United States Department of Pediatrics Dvision of Genetic Medicine University of Washington SeattleWA United States Brotman Baty Institute for Precision Medicine University of Washington SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Howard Hughes Medical Institute University of Washington SeattleWA United States Department of Pediatrics Baylor College of Medicine HoustonTX United States Center for Genomic Medicine Massachusetts General Hospital BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard BostonMA United States Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA United States Stanley Center for

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data science Analysis, Visualization, and informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://***/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics. © 2024, CC BY.

关键词： Genome

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Ccdh: Complexity Based Causal Discovery of Hidden Common Cause (and Other Beasts) in Time Series

SSRN

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SSRN 2022年

作者： Varga, Bálint Stippinger, Marcell Benkő, Zsigmond Fabó, Dániel Halász, Péter Erőss, Loránd Somogyvári, Zoltán Telcs, András Department of Computational Sciences Wigner Research Centre for Physics BudapestH-1121 Hungary János Szentágothai Doctoral School of Neurosciences Semmelweis University BudapestH-1085 Hungary Epilepsy Center Juhász Pál National Institute of Clinical Neurosciences BudapestH-1145 Hungary EPI-HELP BudapestH-1012 Hungary Department of Functional Neurosurgery National Institute of Clinical Neurosciences BudapestH-1145 Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University BudapestH-1083 Hungary Neuromicrosystems ltd. BudapestH-1113 Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics BudapestH-1111 Hungary Department of Quantitative Methods University of Pannonia Faculty of Business and Economics VeszprémH-8200 Hungary

Non-interventional causal discovery is a challenging task. This is particularly true for causal relations of dynamic systems with chaotic attractors. The known solutions are data-intensive and cannot detect a hidden common driver (common cause or confounder). We propose a new method which eliminates both *** method is based on Takens' embedding theorem and complexity of the rank order patterns of the embedded series measured by the compressibility usingnon-sequential recursive pair substitution. The method works on shortsamples and is capable of detecting all kinds of causal connection (including hidden common cause). The newmethod is validated on synthetic data sets and applied to two types of humanelectrophysiological data from epileptic patients. In bothcases, the method revealed self-consistent, contradiction-free and biologically relevant causal connections between the investigatedareas. Thus the method may provide additional indication for the location ofintervention for the decision making medical panel. Given the method advantages and simplicity it might be applied in many other scientific and practical areas with success. © 2022, The Authors. All rights reserved.

关键词： Time series

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Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Glioblastoma Patients

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Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 2025年第9期38卷 100797页

作者： Bhakti Baheti Sunny Rai Shubham Innani Garv Mehdiratta W Robert Bell Sharath Chandra Guntuku MacLean P Nasrallah Spyridon Bakas Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine Indianapolis IN USA Indiana University Melvin and Bren Simon Comprehensive Cancer Center Indianapolis IN USA. Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania Philadelphia PA USA Leonard Davis Institute of Health Economics University of Pennsylvania Philadelphia PA USA. Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine Indianapolis IN USA. School of Arts and Sciences University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Indiana University Melvin and Bren Simon Comprehensive Cancer Center Indianapolis IN USA Department of Neurological Surgery Indiana University School of Medicine Indianapolis IN USA Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Indianapolis IN United States. Electronic address: spbakas@iu.edu.

Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, with a grim prognosis and heterogeneous morphologic and molecular profiles. Since the adoption of the current standard-of-care treatment in 2005, no substantial prognostic improvement has been noticed. In this study, we seek the identification of prognostically relevant glioblastoma characteristics from routinely acquired hematoxylin and eosin-stained whole slide images (WSI) and clinical data, that integrated via advanced computational methods could yield improved patient prognostic stratification and hence optimize clinical decision making and patient management. The proposed WSI analysis capitalizes on a comprehensive curation of apparent artifactual content and an interpretability mechanism via a weakly supervised attention-based multiple- instance learning approach that further utilizes clustering to constrain the search space. Patterns automatically identified by our approach as of high prognostic value classify each WSI as representative of short or long survivors. Further assessments of the prognostic relevance of the associated clinical patient data are performed both in isolation and in an integrated manner, using XGBoost and shapley additive explanations (SHAP). The multimodal integration of WSI with clinical data yields enhanced stratification performance when compared with using either one of the modalities. Identifying tumor morphological and clinical patterns associated with short and long survival will enable the clinical neuropathologist to provide additional relevant prognostic information to the treating team and suggest avenues of biological investigation for further understanding and potentially treating glioblastoma.

关键词： Glioblastoma Histopathology Interpretability Multimodal Prognosis Survival XGBoost

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Deep Brain Neural Network Model Application Using Rabi Antenna Probes as Quantum Consciousness Sensors

SSRN

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SSRN 2022年

作者： Bunruangses, Montree Youplao, Phichai Garhwal, Anita Ray, Kanad Roy, Arup Pham, Nhat Truong Prabpal, Prakasit Yupapin, Preecha Jalil, Muhammad Arif Ali, Jalil Kaiser, Shamim Mahmud, Mufti Mallik, Saurav Zhao, Zhongming Department of Computer Engineering Faculty of Industrial Education Rajamangala University of Technology Phra Nakhon Bangkok10300 Thailand Faculty of Industrial Technology Rajamangala University of Technology Isan Sakon Nakhorn Campus Sakon Nakhon547000 Thailand Asia Metropolitan University 6 Jalan Lembah Bandar Baru Seri Alam 81750 Johor Masai Malaysia Amity University Jaipur Rajasthan India Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Puebla San Manuel Ciudad Universitaria Av. San Claudio y Av. 18 sur Col. Puebla Pue 72570 Mexico Faubert Lab Université de Montréal MontréalH3T 1P1 Canada Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Department of Electrical Technology School of Industrial Technology Institute of Vocational Education Northeastern 2 Sakhon Nakhon47000 Thailand Department of Physics Faculty of Science Unversiti Teknologi Malaysia Johor Skudai81310 Malaysia Institute of Information Technology Jahangirnagar University Dhaka Savar1342 Bangladesh Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Human Genetics Center School of Public Health The University of Texas Health Science Center at Houston HoustonTX77030 United States

The plasmonic antenna probe is designed using a silver bar embedded in a modified add-drop filter within a Mach Zehnder interferometer (MZI). The Rabi antenna is formed when the space-time control has reached the two-level system oscillation, which can be applied as a quantum brain-sensingprobe. A photonic neural network is designed using brain-Rabi antenna communication, wherein the transmission is connected by the neurons. The communication signals are transferred by the electron spins (up-down) and tunable Rabi frequency. The hidden variables and deep brain signals can be obtained by the external detection. We have developed a Rabi antenna via simulation using Computer Simulation Technology (CST) software. We have developed a communication device using the Optiwave program (OptiFDTD). The output signal is plotted by the Matlab program using parameters from the results of the OptiFDTD simulation. The proposed antenna resonates in the frequency range of 192THz-202THz with a maximum gain of 22.4dBi. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Moreover, an intelligent machine learning algorithm is proposed for the identification of high-quality transmission and predicting the behavior of the transmission in near future. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Finally, human quantum consciousness, dreams and human thoughts can be retrieved and discovered. © 2022, The Authors. All rights reserved.

关键词： Antennas

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Deep-learning-driven event reconstruction applied to simulated data from a single Large-Sized Telescope of CTA 37

Deep-learning-driven event reconstruction applied to simulat...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Depaoli, D. Di Pierro, F. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Morselli, A. Mrakovcic, K. Murase, K. Nagai, A. Nakamori, T. Nickel, L. LISTIC Université Savoie Mont-Blanc Polytech Annecy-Chambéry 5 chemin de bellevue Annecy74940 France Institute for Cosmic Ray Research University of Tokyo Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Italy INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France The Barcelona Institute of Science and Technology Spain IRFU CEA Université Paris-Saclay France Laboratoire d'Annecy de Physique des Particules Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS LAPP 9 chemin de bellevue Annecy74940 France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Instituto de Física de Partículas y del Cosmos Departamento de EMFTEL Universidad Complutense de Madrid Madrid Spain INAF Osservatorio di Astrofisica e Scienza Spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Università degli Studi di Padova Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik Germany Department of Physics TU Dortmund University Germany Saha Institute of Nuclear Physics INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Switzerland Palacky University Olomouc Faculty of Science Czech Republic CIEMAT Port d'Informació Científica INFN Sezione di Torino Italy Dipartimento di Fisica Universitá degli Studi di Torino Italy INFN Sezione di Bari Università di Bari Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engin

When very-high-energy gamma rays interact high in the Earth's atmosphere, they produce cascades of particles that induce flashes of Cherenkov light. Imaging Atmospheric Cherenkov Telescopes (IACTs) detect these flashes and convert them into shower images that can be analyzed to extract the properties of the primary gamma ray. The dominant background for IACTs is comprised of air shower images produced by cosmic hadrons, with typical noise-to-signal ratios of several orders of magnitude. The standard technique adopted to differentiate between images initiated by gamma rays and those initiated by hadrons is based on classical machine learning algorithms, such as Random Forests, that operate on a set of handcrafted parameters extracted from the images. Likewise, the inference of the energy and the arrival direction of the primary gamma ray is performed using those parameters. State-of-the-art deep learning techniques based on convolutional neural networks (CNNs) have the potential to enhance the event reconstruction performance, since they are able to autonomously extract features from raw images, exploiting the pixel-wise information washed out during the parametrization process. Here we present the results obtained by applying deep learning techniques to the reconstruction of Monte Carlo simulated events from a single, next-generation IACT, the Large-Sized Telescope (LST) of the Cherenkov Telescope Array (CTA). We use CNNs to separate the gamma-ray-induced events from hadronic events and to reconstruct the properties of the former, comparing their performance to the standard reconstruction technique. Three independent implementations of CNN-based event reconstruction models have been utilized in this work, producing consistent results. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Hadrons

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Biomedical discovery through the integrative biomedical knowledge hub (iBKH)

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iscience 2023年第4期26卷 106460页

作者： Su, Chang Hou, Yu Zhou, Manqi Rajendran, Suraj Maasch, Jacqueline R.M. A. Abedi, Zehra Zhang, Haotan Bai, Zilong Cuturrufo, Anthony Guo, Winston Chaudhry, Fayzan F. Ghahramani, Gregory Tang, Jian Cheng, Feixiong Li, Yue Zhang, Rui DeKosky, Steven T. Bian, Jiang Wang, Fei Department of Health Service Administration and Policy College of Public Health Temple University Philadelphia 19122 PA United States Department of Population Health Sciences Weill Cornell Medicine New York 10065 NY United States Department of Surgery University of Minnesota Minneapolis 55455 MN United States Department of Computational Biology Cornell University Ithaca 14850 NY United States Tri-Institutional Computational Biology & Medicine Program Cornell University New York 10065 NY United States Department of Computer Science Cornell Tech New York 10044 NY United States Department of Physiology and Biophysics Weill Cornell Medicine New York 10065 NY United States Computer Science Cornell University Ithaca 14850 NY United States Department of Medicine Weill Cornell Medicine New York 10021 NY United States Mila-Quebec AI Institute and HEC Montreal Montreal H2S 3H1 QC Canada Genomic Medicine Institute Lerner Research Institute Cleveland Clinic Cleveland 44195 OH United States Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland 44195 OH United States Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland 44106 OH United States School of Computer Science McGill University Montreal H3A 0C6 QC Canada Department of Neurology College of Medicine University of Florida Gainesville 32610 FL United States Department of Health Outcomes & Biomedical Informatics College of Medicine University of Florida Gainesville 32610 FL United States

The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available. © 2023 The Authors

关键词： Bioinformatics Drugs Systems biology

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