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Current and future directions in network biology

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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

The MAPLE package TDDS for computing Thomas decompositions of systems of nonlinear PDEs

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arXiv 2018年

作者： Gerdt, Vladimir P. Lange-Hegermann, Markus Robertz, Daniel Laboratory of Information Technologies Joint Institute for Nuclear Research 6 Joliot-Curie St Dubna141980 Russia Russian Federation and Peoples Friendship University of Russia RUDN University 6 Miklukho-Maklaya St Moscow117198 Russia Department of Electrical Engineering and Computer Science Ostwestfalen-Lippe University of Applied Sciences Liebigstrabe 87 Lemgo32657 Germany School of Computing Electronics and Mathematics Plymouth University 2-5 Kirkby Place Drake Circus PlymouthPL48AA United Kingdom

We present the Maple package TDDS (Thomas Decomposition of Differential Systems). Given a polynomially nonlinear differential system, which in addition to equationsmay contain inequations, this package computes a decomposition of it into a finite set of differentially triangular and algebraically simple subsystems whose subsets of equations are involutive. Usually the decomposed system is substantially easier to investigate and solve both analytically and numerically. The distinctive property of a Thomas decomposition is disjointness of the solution sets of the output subsystems. Thereby, a solution of a well-posed initial problem belongs to one and only one output subsystem. The Thomas decomposition is fully algorithmic. It allows to perform important elements of algebraic analysis of an input differential system such as: Verifying consistency, i.e., the existence of solutions;detecting the arbitrariness in the general analytic solution;given an additional equation, checking whether this equation is satisfied by all common solutions of the input system;eliminating a part of dependent variables from the system if such elimination is possible;revealing hidden constraints on dependent variables, etc. Examples illustrating the use of the package are given. Copyright © 2018, The Authors. All rights reserved.

关键词： Differential equations

Convective carbon dioxide dissolution in a closed porous medium at high-pressure real-gas conditions

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arXiv 2021年

作者： Wen, Baole Shi, Zhuofan Jessen, Kristian Hesse, Marc A. Tsotsis, Theodore T. Department of Mathematics University of Michigan 530 Church Street Ann ArborMI48109 United States Department of Geological Sciences Jackson School of Geosciences The University of Texas at Austin 2275 Speedway Stop C9000 AustinTX78712 United States Mork Family Department of Chemical Engineering and Material Science University of Southern California 925 Bloom Walk HED 216 Los AngelesCA90089 United States Oden Institute for Computational Engineering and Sciencess The University of Texas at Austin 201 E 24th Street AustinTX78712 United States

We combine modeling and measurements to investigate the dynamics of convective carbon dioxide (CO2) dissolution in a pressure-volume-temperature cell, extending a recent study by Wen et al. (J. Fluid Mech., vol. 854, 2018, pp. 56–87) at low-pressure under ideal-gas conditions to high-pressure and real-gas conditions. Pressure-dependent compressibility and solubility are included to model the evolution of CO2 concentration in the gas phase and at the interface, respectively. Simple ordinary-differential-equation models are developed to capture the mean behavior of the convecting system at large Rayleigh number and are then verified by using both numerical simulations and laboratory experiments. The prefactor for the linear scaling of convective CO2 dissolution is evaluated – for the first time – by using pressure-decay experiments in bead packs at reservoir conditions. The results show that our models could quantitatively predict the process of the convective CO2 dissolution in pressure-decay experiments. Moreover, the results also reveal that for increasing gas pressure in closed systems, the negative feedback of the pressure drop – resulting from the dissolution of CO2 in the liquid – is weakened due to the decrease of the solubility constant at real-gas conditions. Our analysis provides a new direction for determination and validation of the convective dissolution flux of CO2 in porous media systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Carbon dioxide

Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

Opportunities for nuclear physics & quantum information science

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arXiv 2019年

作者： Cloët, Ian C. Dietrich, Matthew R. Cloët, Ian C. Dietrich, Matthew R. Arrington, John Bazavov, Alexei Bishof, Michael Freese, Adam Gorshkov, Alexey V. Grassellino, Anna Hafidi, Kawtar Jacob, Zubin McGuigan, Michael Meurice, Yannick Meziani, Zein-Eddine Mueller, Peter Muschik, Christine Osborn, James Otten, Matthew Petreczky, Peter Polakovic, Tomas Poon, Alan Pooser, Raphael Roggero, Alessandro Saffman, Mark VanDevender, Brent Zhang, Jiehang Zohar, Erez Physics Division Argonne National Laboratory ArgonneIL60439 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Joint Quantum Institute NIST/University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science NIST/University of Maryland College ParkMD20742 United States Fermi National Accelerator Laboratory Batavia IL60510 United States Northwestern University EvanstonIL60208 United States Birck Nanotechnology Center and Purdue Quantum Center School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Department of Physics and Astronomy University of Iowa Iowa CityIA52242 United States Institute for Quantum Computing and Department of Physics and Astronomy University of Waterloo West Waterloo ON Canada ALCF Argonne National Laboratory ArgonneIL60439 United States Nanoscience and Technology Division Argonne National Laboratory ArgonneIL60439 United States Physics Department Brookhaven National Laboratory UptonNY11973 United States Physics and Material Science Divisions Argonne National Laboratory ArgonneIL60439 United States Institute for Nuclear and Particle Astrophysics and Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Quantum Information Science Group Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Institute for Nuclear Theory University of Washington SeattleWA98195 United States Department of Physics University of Wisconsin MadisonWI53706 United States Pacific Northwest National Laboratory RichlandWA99354 United States Max Planck Institute of Quantum Opti

This whitepaper is an outcome of the workshop Intersections between Nuclear Physics and Quantum Information held at Argonne National Laboratory on 28-30 March 2018 [***/npqi2018/]. The workshop brought together 116 national and international experts in nuclear physics and quantum information science to explore opportunities for the two fields to collaborate on topics of interest to the U.S. Department of Energy (DOE) Office of science, Office of Nuclear Physics, and more broadly to U.S. society and industry. The workshop consisted of 22 invited and 10 contributed talks, as well as three panel discussion sessions. Topics discussed included quantum computation, quantum simulation, quantum sensing, nuclear physics detectors, nuclear many-body problem, entanglement at collider energies, and lattice gauge theories. Copyright © 2019, The Authors. All rights reserved.

关键词： Lattice theory

Testing whether a Learning Procedure is Calibrated

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arXiv 2020年

作者： Cockayne, Jon Graham, Matthew M. Oates, Chris J. Sullivan, T.J. Teymur, Onur Mathematical Sciences University of Southampton Highfield SouthamptonSO17 1BJ United Kingdom Centre for Advanced Research Computing University College London Gower Street LondonWC1E 6BT United Kingdom School of Mathematics Statistics & Physics Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United Kingdom Mathematics Institute School of Engineering University of Warwick CoventryCV4 7AL United Kingdom School of Mathematics Statistics & Actuarial Science University of Kent CanteburyCT2 7NZ United Kingdom

A learning procedure takes as input a dataset and performs inference for the parameters θ of a model that is assumed to have given rise to the dataset. Here we consider learning procedures whose output is a probability distribution, representing uncertainty about θ after seeing the dataset. Bayesian inference is a prime example of such a procedure, but one can also construct other learning procedures that return distributional output. This paper studies conditions for a learning procedure to be considered calibrated, in the sense that the true data-generating parameters are plausible as samples from its distributional output. A learning procedure whose inferences and predictions are systematically over- or under-confident will fail to be calibrated. On the other hand, a learning procedure that is calibrated need not be statistically efficient. A hypothesis-testing framework is developed in order to assess, using simulation, whether a learning procedure is calibrated. Several vignettes are presented to illustrate different aspects of the framework. © 2020, CC BY.

关键词： Probability distributions

Local viscoelastic properties and shear stress propagation in bulk and confined polymer melts and low-molecular weight liquids

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Physical Review Research 2024年第2期6卷 023161-023161页

作者： Alireza F. Behbahani Petra Bačová Patrycja Polińska Craig Burkhart Manolis Doxastakis Vagelis Harmandaris Institut für Physik Johannes Gutenberg-Universität Mainz Staudingerweg 7 D-55099 Mainz Germany Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion GR-71110 Greece Departamento de Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica Facultad de Ciencias IMEYMAT Campus Universitario Río San Pedro s/n. Puerto Real Cádiz 11510 Spain Goodyear S.A. Avenue Gordon Smith Colmar-Berg L-7750 Luxembourg The Goodyear Tire & Rubber Company 142 Goodyear Boulevard Akron Ohio 44305 USA Department of Chemical and Biomolecular Engineering University of Tennessee Knoxville Tennessee 37996 USA Computation-based Science and Technology Research Center The Cyprus Institute Nicosia 2121 Cyprus Department of Mathematics and Applied Mathematics University of Crete Heraklion GR-71110 Greece

Through the analysis of the spatial correlations of local stress, we detect the propagation of long-ranged liquid-elasticity-mediated shear stress waves in polymeric and low-molecular weight liquids. The propagation of shear waves is effectively planar; i.e., σαβ propagates in the αβ plane. The autocorrelation functions of the local stress of a region are affected by both the relaxation of stress in that region and the propagation of stress from the region to the rest of the sample. However, due to the planar propagation of shear waves, the transfer of σαβ from those slices of the simulation box that are periodic in the αβ plane is negligible. This allows direct probing of the position-dependent local stress relaxation modulus of liquid in the vicinity of a confining surface.

关键词： Confinement Non-Newtonian fluids Physisorption Polymer behavior Polymer conformation & topology Rheological properties

A short perspective on a posteriori error control and adaptive discretizations

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arXiv 2024年

作者： Becker, Roland Bordas, Stéphane P.A. Chouly, Franz Omnes, Pascal LMAP University of Pau IPRA BP 1155 Av. de l’Université Pau64013 France Institute for Computational Engineering Faculty of Science Technology and Communication University of Luxembourg Luxembourg University of the Republic Faculty of Science Center of Mathematics Montevideo11400 Uruguay Université Sorbonne Paris Nord LAGA CNRS UMR 7539 Institut Galilée 99 Av. J.-B. Clément Villetaneuse93430 France Université Paris-Saclay CEA Service de Génie Logiciel pour la Simulation Gif-sur-Yvette91191 France

Error control by means of a posteriori error estimators or indicators and adaptive discretizations, such as adaptive mesh refinement, have emerged in the late seventies. Since then, numerous theoretical developments and improvements have been made, as well as the first attempts to introduce them into real-life industrial applications. The present introductory chapter provides an overview of the subject, highlights some of the achievements to date and discusses possible perspectives. © 2024, CC BY.

关键词： Error analysis

EAI/Springer Innovations in Communication and Computing

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Preface

EAI/Springer Innovations in Communication and computing 2020年 vii页

作者： Shandilya, Shishir K. Nagar, Atulya K. Wagner, Neal School of Computing Science & Engineering Vellore Institute of Technology VIT Bhopal University BhopalMadhya Pradesh India School of Mathematics Computer Science and Engineering Faculty of Science Liverpool Hope University Liverpool United Kingdom Analytics and Intelligence Division Systems and Technology Research WoburnMA United States