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The need to implement FAIR principles in biomolecular simulations

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arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

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

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IEEE Transactions on Technology and Society

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

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

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

关键词： COVID-19

Correction: An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory

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Soft Computing 2022年第22期26卷 12865-12865页

作者： Haji Seyed Asadollah, Seyed Babak Sharafati, Ahmad Haghbin, Masoud Motta, Davide Hosseinian Moghadam Noghani, Mohamadreza Department of Environmental Resources Engineering State University of New York College of Environmental Science and Forestry Syracuse USA Department of Civil Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Structural Mechanics and Hydraulics Engineering Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada (UGR) Granada Spain Department of Mechanical and Construction Engineering Northumbria University Newcastle upon Tyne UK

Interplay Between Residential Nature Exposure and Walkability and Their Association with Cardiovascular Health

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JACC: Advances

JACC: Advances 2025年第1期4卷 101457-101457页

作者： Makram, Omar M. Nwana, Nwabunie Pan, Alan Nicolas, Juan C. Gullapelli, Rakesh Bose, Budhaditya Sabharwal, Ashutosh Chang, Jenny Javed, Zulqarnain Kash, Bita Maddock, Jay E. Nasir, Khurram Al-Kindi, Sadeer Center for Health & Nature Houston Methodist Research Institute Houston TX United States Department of Medicine Medical College of Georgia at Augusta University Augusta GA United States Center for Health Data Science and Analytics Houston Methodist Research Institute Houston TX United States Department of Electrical and Computer Engineering Rice University Houston TX United States Houston Methodist Neal Cancer Center Houston Methodist Hospital Houston TX United States Department of Environmental and Occupational Health School of Public Health Texas A&M University College Station TX United States Division of Cardiovascular Prevention and Wellness Department of Cardiology Houston Methodist DeBakey Heart and Vascular Center Houston TX United States Center for Cardiovascular Computational Health & Precision Medicine (C3-PH) Houston Methodist Hospital Houston TX United States

Background: Green space has been linked with cardiovascular (CV) health. Nature access and quality may have significant impact on CV risk factors and health. Objectives: The authors aimed to investigate the relationship between NatureScore, a composite score for natural environment exposure and quality of green spaces, with CV risk factors and atherosclerotic cardiovascular diseases (ASCVD). Methods: A cross-sectional study including one million adult patients from the Houston Methodist Learning Health System Outpatient Registry (2016-2022). NatureScore is a composite measure of natural environment exposure and quality (0-100) calculated for each patient based on residential address. NatureScores was divided into 4 categories: nature deficient/light (0-39), nature adequate (40-59), nature rich (60-79), and nature utopia (80-100). CV risk factors included hypertension, diabetes, dyslipidemia, and obesity. Results: Among 1.07 million included patients (mean age 52 years, female 59%, Hispanic 16%, Non-Hispanic Black 14%), median NatureScore was 69.4. After adjusting for neighborhood walkability, patients living in highest NatureScore neighborhoods had lower prevalence of CV risk factors (OR: 0.91, 95% CI: 0.90-0.93) and ASCVD (OR: 0.96, 95% CI: 0.93-0.98) than those in lowest NatureScore neighborhoods. A significant interaction existed between NatureScore and Walkability (P < 0.001), where those in high NatureScore (≥60) high walkability (≥40) areas had lower prevalence of CV risk factors (OR: 0.93, 95% CI: 0.90-0.97, P < 0.001) and were more likely to have optimal CV risk profile (relative risk ratio: 1.09, 95% CI: 1.04-1.14, P = 0.001). Conclusions: These findings suggest that while green spaces benefit health, their accessibility through walkable environments is crucial for cardiovascular disease protection. © 2024 The Authors

关键词： cardiovascular diabetes hypertension nature walkability

The discovery of a rotating radio transient J1918−0449 with intriguing emission properties with the five hundred meter aperture spherical radio telescope

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arXiv 2022年

作者： Chen, J.L. Wen, Zhigang Yuan, J.P. Wang, N. Li, D. Wang, H.G. Yan, W.M. Yuen, R. Wang, P. Wang, Z. Zhu, W.W. Niu, J.R. Miao, C.C. Xue, M.Y. Gong, B.P. Department of Physics and Electronic Engineering Yuncheng University Shanxi Yuncheng044000 China Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science-1 Street Xinjiang Urumqi830011 China Key laboratory of Radio Astronomy Chinese Academy of Sciences Nanjing210008 China Key Laboratory of Microwave Technology Urumqi Xinjiang830011 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Guiyang550001 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-Natal Durban4000 South Africa School of Physics and Electronic Engineering Guangzhou University Guangzhou510006 China School of Physical Science and Technology Xinjiang University Xinjiang Urumqi830046 China Department of Physics Huazhong University of Science and Technology Wuhan430074 China

In this study, we report on a detailed single pulse analysis of the radio emission from a rotating radio transient (RRAT) J1918−0449 which is the first RRAT discovered with the five hundred meter aperture spherical radio telescope (FAST). The sensitive observations were carried out on 30 April 2021 using the FAST with a central frequency of 1250 MHz and a short time resolution of 49.152 µs, which forms a reliable basis to probe single pulse emission properties in detail. The source was successively observed for around 2 hours. A total of 83 dispersed bursts with significance above 6σ are detected over 1.8 hours. The source’s DM and rotational period are determined to be 116.1±0.4 pc cm−3 and 2479.21±0.03 ms, respectively. The share of registered pulses from the total number of observed period is 3.12%. No underlying emission is detected in the averaged off pulse profile. For bursts with fluence larger than 10 Jy ms, the pulse energy follows a power-law distribution with an index of −3.1 ± 0.4, suggesting the existence of bright pulse emission. We find that the distribution of time between subsequent pulses is consistent with a stationary Poisson process and find no evidence of clustering over the 1.8 h observations, giving a mean burst rate of one burst every 66 s. Close inspection of the detected bright pulses reveals that 21 pulses exhibit well-defined quasi-periodicities. The subpulse drifting is present in non-successive rotations with periodicity of 2.51 ± 0.06 periods. Finally, possible physical mechanisms are discussed. © 2022, CC BY.

关键词： Poisson distribution

Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography

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arXiv 2023年

作者： Zhang, Linglin Brown-Mulry, Beatrice Nalla, Vineela Hwang, InChan Gichoya, Judy Wawira Gastounioti, Aimilia Banerjee, Imon Seyyed-Kalantari, Laleh Woo, MinJae Trivedi, Hari School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Information Technology Kennesaw State University 1100 South Marietta Pkwy MariettaGA30060 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Computational Imaging Research Center Washington University in St. Louis School of Medicine 4525 Scott Avenue St. LouisMO63110 United States Department of Radiology Mayo Clinic Arizona 13400 E Shea Blvd ScottsdaleAZ85259 United States School of Computing and Augmented Intelligence Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada

Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with increased risk of model failure remain unclear. This retrospective study uses the Emory BrEast Imaging dataset (EMBED) containing mammograms from 115,931 patients imaged at Emory Healthcare between 2013-2020, with Breast Imaging Reporting and data System (BI-RADS) assessment, region of interest coordinates for abnormalities, imaging features, pathologic outcomes, and patient demographics. Multiple deep learning models were trained to distinguish between abnormal tissue patches and randomly selected normal tissue patches from screening mammograms. We assessed model performance by subgroups defined by age, race, pathologic outcome, tissue density, and imaging characteristics and investigated their associations with false negatives and false positives. We also performed multivariate logistic regression to control for confounding between subgroups. The top-performing model, ResNet152V2, achieved accuracy of 92.6% (95%CI=92.0-93.2%), and area under the receiver operating characteristics curve 0.975 (95%CI=0.972-0.978). Before controlling for confounding, nearly all subgroups showed statistically significant differences in model performance. However, after controlling for confounding, we found lower false negative risk associates with Other race (RR=0.828;p= .050), biopsy-proven benign lesions (RR=0.927;p= .011), and mass (RR=0.921;p= .010) or asymmetry (RR=0.854;p= .040);higher false negative risk associates with architectural distortion (RR=1.037;p Copyright © 2023, The Authors. All rights reserved.

关键词： Mammography

Biases in inverse Ising estimates of near-critical behavior

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Physical Review E 2023年第1期108卷 014109-014109页

作者： Maximilian B. Kloucek Thomas Machon Shogo Kajimura C. Patrick Royall Naoki Masuda Francesco Turci School of Physics HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Bristol Centre for Functional Nanomaterials HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto 606-8585 Japan Gulliver UMR CNRS 7083 ESPCI Paris Université PSL 75005 Paris France Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.

关键词： Critical phenomena Cortical networks Spin glasses Functional magnetic resonance imaging Ising model

A continuum and computational framework for viscoelastodynamics: Finite deformation linear models

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arXiv 2021年

作者： Liu, Ju Latorre, Marcos Marsden, Alison L. Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen Guangdong518055 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen Guangdong518055 China Institute for Computational and Mathematical Engineering Stanford University Clark Center E1.3 318 Campus Drive StanfordCA94305 United States Department of Biomedical Engineering Yale University New HavenCT06520 United States Department of Bioengineering Stanford University Clark Center E1.3 318 Campus Drive StanfordCA94305 United States

This work concerns the continuum basis and numerical formulation for deformable materials with viscous dissipative mechanisms. We derive a viscohyperelastic modeling framework based on fundamental thermomechanical principles. Since most large deformation problems exhibit isochoric properties, our modeling work is constructed based on the Gibbs free energy in order to develop a continuum theory using pressure-primitive variables, which is known to be well-behaved in the incompressible limit. A set of general evolution equations for the internal state variables is derived. With that, we focus on a family of free energies that leads to the so-called finite deformation linear model. Our derivation elucidates the origin of the evolution equations of that model, which was originally proposed heuristically and thus lacked formal compatibility with the underlying thermodynamics. In our derivation, the thermodynamic inconsistency is clarified and rectified. A classical model based on the identical polymer chain assumption is revisited and is found to have non-vanishing viscous stresses in the equilibrium limit, which is counter-intuitive in the physical sense. Because of that, we then discuss the relaxation property of the non-equilibrium stress in the thermodynamic equilibrium limit and its implication on the form of free energy. A modified version of the identical polymer chain model is then proposed, with a special case being the model proposed by G. Holzapfel and J. Simo. Based on the consistent modeling framework, a provably energy stable numerical scheme is constructed for incompressible viscohyperelasticity using inf-sup stable elements. In particular, we adopt a suite of smooth generalization of the Taylor-Hood element based on Non-Uniform Rational B-Splines (NURBS) for spatial discretization. The temporal discretization is performed via the generalized-α scheme. We present a suite of numerical results to corroborate the proposed numerical properties, including the n

关键词： Free energy

Finding shortest and nearly shortest path nodes in large substantially incomplete networks

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins