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Xu X, Zhou J, Zhu C, Zhan Q, Li Z, Zhang R, Wang Y, Liao X, Gao X. Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning. F1000Res 2024; 12:757. [PMID: 38434657 PMCID: PMC10905145 DOI: 10.12688/f1000research.130936.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/15/2024] [Indexed: 03/05/2024] Open
Abstract
Background The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. Methods In this study, we developed SGPT-RL, which uses a generative pre-trained transformer (GPT) as the policy network of the reinforcement learning (RL) agent to optimize the binding affinity to a target. SGPT-RL was evaluated on the Moses distribution learning benchmark and two goal-directed generation tasks, with Dopamine Receptor D2 (DRD2) and Angiotensin-Converting Enzyme 2 (ACE2) as the targets. Both QSAR model and molecular docking were implemented as the optimization goals in the tasks. The popular Reinvent method was used as the baseline for comparison. Results The results on the Moses benchmark showed that SGPT-RL learned good property distributions and generated molecules with high validity and novelty. On the two goal-directed generation tasks, both SGPT-RL and Reinvent were able to generate valid molecules with improved target scores. The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal. Further analysis shows that SGPT-RL learned conserved scaffold patterns during exploration. Conclusions The superior performance of SGPT-RL in the ACE2 task indicates that it can be applied to the virtual screening process where molecular docking is widely used as the criteria. Besides, the scaffold patterns learned by SGPT-RL during the exploration process can assist chemists to better design and discover novel lead candidates.
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Affiliation(s)
- Xiaopeng Xu
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Juexiao Zhou
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Chen Zhu
- KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Qing Zhan
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Zhongxiao Li
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | | | - Yu Wang
- Syneron Technology, Guangzhou, China
| | - Xingyu Liao
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
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2
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Bai J, Wang J. Modeling long COVID dynamics: Impact of underlying health conditions. J Theor Biol 2024; 576:111669. [PMID: 37977479 PMCID: PMC10754059 DOI: 10.1016/j.jtbi.2023.111669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/03/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023]
Abstract
We propose a new mathematical model to investigate the population dynamics of long COVID, with a focus on the impact of chronic health conditions. Our model connects long COVID with the transmission of COVID-19 so as to accurately predict the prevalence of long COVID from the progression of the infection in the host population. The model additionally incorporates the effects of COVID-19 vaccination. We implement the model with data from both the US and the UK to demonstrate the real-world applications of this modeling framework.
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Affiliation(s)
- Jie Bai
- School of Mathematics and Statistics, Liaoning University, Shenyang 110036, China.
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga TN 37403, USA.
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3
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Derrick J, Patterson B, Bai J, Wang J. A Mechanistic Model for Long COVID Dynamics. MATHEMATICS (BASEL, SWITZERLAND) 2023; 11:4541. [PMID: 38111916 PMCID: PMC10727852 DOI: 10.3390/math11214541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Long COVID, a long-lasting disorder following an acute infection of COVID-19, represents a significant public health burden at present. In this paper, we propose a new mechanistic model based on differential equations to investigate the population dynamics of long COVID. By connecting long COVID with acute infection at the population level, our modeling framework emphasizes the interplay between COVID-19 transmission, vaccination, and long COVID dynamics. We conducted a detailed mathematical analysis of the model. We also validated the model using numerical simulation with real data from the US state of Tennessee and the UK.
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Affiliation(s)
- Jacob Derrick
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
| | - Ben Patterson
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
| | - Jie Bai
- School of Mathematics and Statistics, Liaoning University, Shenyang 110036, China
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA
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4
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Cheng Z, Lai Y, Jin K, Zhang M, Wang J. Modeling the XBB strain of SARS-CoV-2: Competition between variants and impact of reinfection. J Theor Biol 2023; 574:111611. [PMID: 37640233 PMCID: PMC10592017 DOI: 10.1016/j.jtbi.2023.111611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/16/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023]
Abstract
XBB, an Omicron subvariant of SARS-CoV-2 that began to circulate in late 2022, has been dominant in the US since early 2023. To quantify the impact of XBB on the progression of COVID-19, we propose a new mathematical model which describes the interplay between XBB and other SARS-CoV-2 variants at the population level and which incorporates the effects of reinfection. We apply the model to COVID-19 data in the US that include surveillance data on the cases and variant proportions from the New York City, the State of New York, and the State of Washington. Our fitting and simulation results show that the transmission rate of XBB is significantly higher than that of other variants and the reinfection from XBB may play an important role in shaping the pandemic/epidemic pattern in the US.
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Affiliation(s)
- Ziqiang Cheng
- School of Mathematics, Hefei University of Technology, Hefei, Anhui 230009, China
| | - Yinglei Lai
- School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Kui Jin
- Department of Emergency Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Mengping Zhang
- School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA.
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5
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Shakibfar S, Nyberg F, Li H, Zhao J, Nordeng HME, Sandve GKF, Pavlovic M, Hajiebrahimi M, Andersen M, Sessa M. Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review. Front Public Health 2023; 11:1183725. [PMID: 37408750 PMCID: PMC10319067 DOI: 10.3389/fpubh.2023.1183725] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/31/2023] [Indexed: 07/07/2023] Open
Abstract
Aim To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources. Study eligibility criteria Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data sources Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment A bias assessment of AI models was done using PROBAST. Participants Patients tested positive for COVID-19. Results We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.
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Affiliation(s)
- Saeed Shakibfar
- Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Huiqi Li
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jing Zhao
- Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, Oslo, Norway
| | - Hedvig Marie Egeland Nordeng
- Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, Oslo, Norway
| | - Geir Kjetil Ferkingstad Sandve
- UiORealArt Convergence Environment, University of Oslo, Oslo, Norway
- Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Milena Pavlovic
- UiORealArt Convergence Environment, University of Oslo, Oslo, Norway
- Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | | | - Morten Andersen
- Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Maurizio Sessa
- Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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6
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Zhang J, Zheng N, Liu M, Yao D, Wang Y, Wang J, Xin J. Multi-weight susceptible-infected model for predicting COVID-19 in China. Neurocomputing 2023; 534:161-170. [PMID: 36923265 PMCID: PMC9993734 DOI: 10.1016/j.neucom.2023.02.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/10/2023] [Accepted: 02/26/2023] [Indexed: 03/17/2023]
Abstract
The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3-4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022.
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Affiliation(s)
- Jun Zhang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.,School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Nanning Zheng
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Mingyu Liu
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.,Qian Xuesen College, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Dingyi Yao
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.,Qian Xuesen College, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Yusong Wang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Jianji Wang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Jingmin Xin
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
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7
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Feng S, Park S, Choi YK, Im W. CHARMM-GUI Membrane Builder: Past, Current, and Future Developments and Applications. J Chem Theory Comput 2023; 19:2161-2185. [PMID: 37014931 PMCID: PMC10174225 DOI: 10.1021/acs.jctc.2c01246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Molecular dynamics simulations of membranes and membrane proteins serve as computational microscopes, revealing coordinated events at the membrane interface. As G protein-coupled receptors, ion channels, transporters, and membrane-bound enzymes are important drug targets, understanding their drug binding and action mechanisms in a realistic membrane becomes critical. Advances in materials science and physical chemistry further demand an atomistic understanding of lipid domains and interactions between materials and membranes. Despite a wide range of membrane simulation studies, generating a complex membrane assembly remains challenging. Here, we review the capability of CHARMM-GUI Membrane Builder in the context of emerging research demands, as well as the application examples from the CHARMM-GUI user community, including membrane biophysics, membrane protein drug-binding and dynamics, protein-lipid interactions, and nano-bio interface. We also provide our perspective on future Membrane Builder development.
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Affiliation(s)
- Shasha Feng
- Departments of Biological Sciences and Chemistry, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Soohyung Park
- Departments of Biological Sciences and Chemistry, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Yeol Kyo Choi
- Departments of Biological Sciences and Chemistry, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences and Chemistry, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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8
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Men K, Li Y, Wang X, Zhang G, Hu J, Gao Y, Han A, Liu W, Han H. Estimate the incubation period of coronavirus 2019 (COVID-19). Comput Biol Med 2023; 158:106794. [PMID: 37044045 PMCID: PMC10062796 DOI: 10.1016/j.compbiomed.2023.106794] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 02/23/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023]
Abstract
COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.
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Affiliation(s)
- Ke Men
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Yihao Li
- The Gabelli School of Business, Fordham University, Lincoln Center, New York, NY, 10023, USA
| | - Xia Wang
- The Air Force Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Guangwei Zhang
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Jingjing Hu
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Yanyan Gao
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Ashley Han
- The Skyline High School, Ann Arbor, MI, 48103, USA
| | - Wenbin Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Henry Han
- The Laboratory of Data Science and Artificial Intelligence Innovation, Department of Computer Science, School of Engineering and Computer Science, Baylor University, Waco, TX, 76789, USA.
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9
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Hasan MM, Islam MU, Sadeq MJ, Fung WK, Uddin J. Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment. SENSORS (BASEL, SWITZERLAND) 2023; 23:527. [PMID: 36617124 PMCID: PMC9824505 DOI: 10.3390/s23010527] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.
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Affiliation(s)
- Md. Mahadi Hasan
- Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia 1349, Bangladesh
| | - Muhammad Usama Islam
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Muhammad Jafar Sadeq
- Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia 1349, Bangladesh
| | - Wai-Keung Fung
- Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
| | - Jasim Uddin
- Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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10
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Truszkowska A, Zino L, Butail S, Caroppo E, Jiang Z, Rizzo A, Porfiri M. Exploring a COVID-19 Endemic Scenario: High-Resolution Agent-Based Modeling of Multiple Variants. ADVANCED THEORY AND SIMULATIONS 2023; 6:2200481. [PMID: 36718198 PMCID: PMC9878004 DOI: 10.1002/adts.202200481] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/29/2022] [Indexed: 11/13/2022]
Abstract
Our efforts as a society to combat the ongoing COVID-19 pandemic are continuously challenged by the emergence of new variants. These variants can be more infectious than existing strains and many of them are also more resistant to available vaccines. The appearance of these new variants cause new surges of infections, exacerbated by infrastructural difficulties, such as shortages of medical personnel or test kits. In this work, a high-resolution computational framework for modeling the simultaneous spread of two COVID-19 variants: a widely spread base variant and a new one, is established. The computational framework consists of a detailed database of a representative U.S. town and a high-resolution agent-based model that uses the Omicron variant as the base variant and offers flexibility in the incorporation of new variants. The results suggest that the spread of new variants can be contained with highly efficacious tests and mild loss of vaccine protection. However, the aggressiveness of the ongoing Omicron variant and the current waning vaccine immunity point to an endemic phase of COVID-19, in which multiple variants will coexist and residents continue to suffer from infections.
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Affiliation(s)
- Agnieszka Truszkowska
- Center for Urban Science and ProgressTandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA,Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA,Department of Chemical and Materials EngineeringUniversity of Alabama in Huntsville301 Sparkman DriveHuntsvilleAL35899USA
| | - Lorenzo Zino
- Engineering and Technology Institute GroningenUniversity of GroningenNijenborgh 4GroningenAG9747The Netherlands,Department of Electronics and TelecommunicationsPolitecnico di TorinoCorso Duca degli Abruzzi 24Turin10129Italy
| | - Sachit Butail
- Department of Mechanical EngineeringNorthern Illinois UniversityDeKalbIL60115USA
| | - Emanuele Caroppo
- Department of Mental HealthLocal Health Unit ROMA 2Rome00159Italy,University Research Center He.R.A.Université Cattolica del Sacro CuoreRome00168Italy
| | - Zhong‐Ping Jiang
- Department of Electrical and Computer EngineeringTandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA
| | - Alessandro Rizzo
- Department of Electronics and TelecommunicationsPolitecnico di TorinoCorso Duca degli Abruzzi 24Turin10129Italy,Institute for InventionInnovation and EntrepreneurshipTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
| | - Maurizio Porfiri
- Center for Urban Science and ProgressTandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA,Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA,Department of Biomedical EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
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11
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Sheu RK, Pardeshi MS. A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System. SENSORS (BASEL, SWITZERLAND) 2022; 22:8068. [PMID: 36298417 PMCID: PMC9609212 DOI: 10.3390/s22208068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of utmost importance. Meanwhile, incorporating explanations in the medical domain with respect to legal and ethical AI is necessary to understand detailed decisions, results, and current status of the patient's conditions. Successively, we will be presenting a detailed survey for the medical XAI with the model enhancements, evaluation methods, significant overview of case studies with open box architecture, medical open datasets, and future improvements. Potential differences in AI and XAI methods are provided with the recent XAI methods stated as (i) local and global methods for preprocessing, (ii) knowledge base and distillation algorithms, and (iii) interpretable machine learning. XAI characteristics details with future healthcare explainability is included prominently, whereas the pre-requisite provides insights for the brainstorming sessions before beginning a medical XAI project. Practical case study determines the recent XAI progress leading to the advance developments within the medical field. Ultimately, this survey proposes critical ideas surrounding a user-in-the-loop approach, with an emphasis on human-machine collaboration, to better produce explainable solutions. The surrounding details of the XAI feedback system for human rating-based evaluation provides intelligible insights into a constructive method to produce human enforced explanation feedback. For a long time, XAI limitations of the ratings, scores and grading are present. Therefore, a novel XAI recommendation system and XAI scoring system are designed and approached from this work. Additionally, this paper encourages the importance of implementing explainable solutions into the high impact medical field.
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Affiliation(s)
- Ruey-Kai Sheu
- Department of Computer Science, Tunghai University, No. 1727, Section 4, Taiwan Blvd, Xitun District, Taichung 407224, Taiwan
| | - Mayuresh Sunil Pardeshi
- AI Center, Tunghai University, No. 1727, Section 4, Taiwan Blvd, Xitun District, Taichung 407224, Taiwan
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12
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Li Y, Zhang D, Gao X, Wang X, Zhang L. 2'- and 3'-Ribose Modifications of Nucleotide Analogues Establish the Structural Basis to Inhibit the Viral Replication of SARS-CoV-2. J Phys Chem Lett 2022; 13:4111-4118. [PMID: 35503748 PMCID: PMC9088111 DOI: 10.1021/acs.jpclett.2c00087] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/27/2022] [Indexed: 05/12/2023]
Abstract
Inhibition of RNA-dependent RNA polymerase (RdRp) by nucleotide analogues with ribose modification provides a promising antiviral strategy for the treatment of SARS-CoV-2. Previous works have shown that remdesivir carrying 1'-substitution can act as a "delayed chain terminator", while nucleotide analogues with 2'-methyl group substitution could immediately terminate the chain extension. However, how the inhibition can be established by the 3'-ribose modification as well as other 2'-ribose modifications is not fully understood. Herein, we have evaluated the potential of several adenosine analogues with 2'- and/or 3'-modifications as obligate chain terminators by comprehensive structural analysis based on extensive molecular dynamics simulations. Our results suggest that 2'-modification couples with the protein environment to affect the structural stability, while 3'-hydrogen substitution inherently exerts "immediate termination" without compromising the structural stability in the active site. Our study provides an alternative promising modification scheme to orientate the further optimization of obligate terminators for SARS-CoV-2 RdRp.
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Affiliation(s)
- Yongfang Li
- State
Key Laboratory of Structural Chemistry, Fujian Institute of Research
on the Structure of Matter, Chinese Academy
of Sciences, 350002, Fuzhou, Fujian, China
- University
of Chinese Academy of Sciences, 100864, Beijing, China
| | - Dong Zhang
- State
Key Laboratory of Structural Chemistry, Fujian Institute of Research
on the Structure of Matter, Chinese Academy
of Sciences, 350002, Fuzhou, Fujian, China
- University
of Chinese Academy of Sciences, 100864, Beijing, China
| | - Xin Gao
- Computational
Bioscience Research Center (CBRC), Computer, Electrical and Mathematical
Sciences and Engineering Division, King
Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Xiaowei Wang
- Department
of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon 999077, Hong Kong
| | - Lu Zhang
- State
Key Laboratory of Structural Chemistry, Fujian Institute of Research
on the Structure of Matter, Chinese Academy
of Sciences, 350002, Fuzhou, Fujian, China
- University
of Chinese Academy of Sciences, 100864, Beijing, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, 361005, Xiamen, Fujian, China
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13
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Napolitano F, Gao X. Special issue on computational biology and bioinformatic applications to the COVID-19 pandemic. QUANTITATIVE BIOLOGY 2022. [DOI: 10.15302/j-qb-022-0293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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14
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Душенков B, Душенкова A. [Not Available]. PAEMI SINO 2022; 24:113-122. [PMID: 36225268 PMCID: PMC9553026 DOI: 10.25005/2074-0581-2022-24-1-113-122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Высокая заболеваемость и смертность от COVID-19 привели к чрезвычайной ситуации в области здравоохранения во всём мире, вызвав активизацию и консолидацию усилий в соответствующих областях научных исследований и практике здравоохранения.
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Affiliation(s)
- B Душенков
- Кафедра естественных наук Колледжа Хостос Коммьюнити, Городской университет Нью-Йорка, Бронкс, Нью-Йорк, США
| | - A Душенкова
- Институт фармации и наук о здоровье, Университет Фэрли Дикинсона, Флорхам Парк, Нью-Джерси, США
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