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COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020.
Mixing autoencoder with classifier: Conceptual data visualization
Hartono, P. (2019) · 1912
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Heuritic self-organization in problems of engineering cybernetics
Ivakhnenko, A. G. (1970) · 1970
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ANFIS: adaptive-network-based fuzzy inference system
Jang, J.-S. R. (1993) · 1993
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Support-vector networks
Cortes, C., and Vapnik, V. (1995) · 1995
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Long short-term memory
Hochreiter, S., and Schmidhuber, J. (1997) · 1997
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Random forests
Breiman, L. (2001) · 2001
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Biochemistry
Berg, J. M., Tymoczko, J. L., and Stryer, L. (2002) · 2002
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Artificial intelligence forecasting of COVID-19 in China
Hu, Z., Ge, Q., Li, S., Jin, L., and Xiong, M. (2020c) · 2002
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Wang, Y., Hu, M., Li, Q., Zhang, X.-P., Zhai, G., and Yao, N. (2020b) · 2002
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Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Abbas, A., Abdelsamea, M. M., and Gaber, M. M. (2020) · 2003
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COVID-19: The first public coronavirus Twitter dataset
Chen, E., Lerman, K., and Ferrara, E. (2020a) · 2003
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The COVID-19 social media infodemic
Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., and Scala, A. (2020) · 2003
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COVID-19 image data collection
Cohen, J. P., Morrison, P., and Dao, L. (2020) · 2003
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Neural network aided quarantine control model estimation of covid spread in Wuhan, China
Dandekar, R., and Barbastathis, G. (2020) · 2003
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Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection
Ghoshal, B., and Tucker, A. (2020) · 2003
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Gozes, O., Frid-Adar, M., Greenspan, H., Browning, P. D., Zhang, H., Ji, W., Bernheim, A., and Siegel, E. (2020a) · 2003
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Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model
Hu, F., Jiang, J., and Yin, P. (2020a) · 2003
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Tracking COVID-19 using online search
Lampos, V., Moura, S., Yom-Tov, E., Edelstein, M., Majumder, M., Hamada, Y., Rangaka, M. X., McKendry, R. A., , and Cox, I. J. (2020) · 2003
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Potential neutralizing antibodies discovered for novel coronavirus using Machine Learning
Magar, R., Yadav, P., and Farimani, A. B. (2020) · 2003
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Maghdid, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., and Rabie, K. (2020) · 2003
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Advertisers jump on coronavirus bandwagon: Politics, news, and business
Mejova, Y., and Kalimeri, K. (2020) · 2003
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A machine learning application for raising WASH awareness in the times of COVID-19 pandemic
Pandey, R., Gautam, V., Bhagat, K., and Sethi, T. (2020) · 2003
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A survey of deep learning for scientific discovery
Raghu, M., and Schmidt, E. (2020) · 2003
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Lung infection quantification of COVID-19 in CT images with deep learning
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., and Shi, Y. (2020) · 2003
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A first look at COVID-19 information and misinformation sharing on Twitter
Singha, L., Bansala, S., Bodea, L., Budakb, C., Chic, G., Kawintiranona, K., Paddena, C., Vanarsdalla, R., Vragad, E., and Wanga, Y. (2020) · 2003
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Tang, Z., Zhao, W., Xie, X., Zhong, Z., Shi, F., Liu, J., and Shen, D. (2020b) · 2003
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Wang, L., and Wong, A. (2020) · 2003
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Information mining for COVID-19 research from a large volume of scientific literature
Ahamed, S., and Samad, M. D. (2020) · 2004
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Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., and Chowell, G. (2020) · 2004
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Screening of Therapeutic Agents for COVID-19 using Machine Learning and Ensemble Docking Simulations
Batra, R., Chan, H., Kamath, G., Ramprasad, R., Cherukara, M. J., and Sankaranarayanan, S. (2020) · 2004
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Target-specific and selective drug design for COVID-19 using deep generative models
Chenthamarakshan, V., Das, P., Padhi, I., Strobelt, H., Lim, K. W., Hoover, B., Hoffman, S. C., and Mojsilovic, A. (2020) · 2004
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Discovering associations in COVID-19 related research papers
Fister, I. J., Fister, K., and Fister, I. (2020) · 2004
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Assessing the risks of “infodemics” in response to COVID-19 epidemics
Gallotti, R., Valle, F., Castaldo, N., Sacco, P., and Domenico, M. D. (2020) · 2004
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Coronavirus detection and analysis on chest CT with deep learning
Gozes, O., Frid-Adar, M., Sagie, N., Zhang, H., Ji, W., and Greenspan, H. (2020b) · 2004
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Deep learning on chest X-ray images to detect and evaluate pneumonia cases at the era of COVID-19
Hammoudi, K., Benhabiles, H., Melkemi, M., Dornaika, F., Arganda-Carreras, I., Collard, D., and Scherpereel, A. (2020) · 2004
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Generating similarity map for COVID-19 transmission dynamics with topological autoencoder
Hartono, P. (2020) · 2004
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Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Hofmarcher, M., Mayr, A., Rumetshofer, E., Ruch, P., Renz, P., Schimunek, J., Seidl, P., Vall, A., Widrich, M., Hochreiter, S., and Klambauer, G. (2020) · 2004
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DeepCOVIDExplainer: Explainable COVID-19 predictions based on chest X-ray images
Karim, M., Döhmen, T., Rebholz-Schuhmann, D., Decker, S., Cochez, M., Beyan, O., et al. (2020) · 2004
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Measuring emotions in the COVID-19 real world worry dataset
Kleinberg, B., van der Vegt, I., and Mozes, M. (2020) · 2004
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COVID-MobileXpert: On-device COVID-19 screening using snapshots of chest X-ray
Li, X., Li, C., and Zhu, D. (2020b) · 2004
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Liu, D., Clemente, L., Poirier, C., Ding, X., Chinazzi, M., David, J. T., Vespignani, A., and Santillana, M. (2020b) · 2004
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Neural network based country wise risk prediction of COVID-19
Pal, R., Sekh, A. A., Kar, S., and Prasad, D. K. (2020) · 2004
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Bayesian semiparametric time varying model for count data to study the spread of the COVID-19 cases
Roy, A., and Karmakar, S. (2020) · 2004
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Schild, L., Ling, C., Blackburn, J., Stringhini, G., Zhang, Y., and Zannettou, S. (2020) · 2004
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Hate multiverse spreads malicious COVID-19 content online beyond individual platform control
Velásquez, N., Leahy, R., Restrepo, N. J., Lupu, Y., Sear, R., Gabriel, N., Jha, O., and Johnson, N. (2020) · 2004
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CORD-19: The COVID-19 open research dataset
Wang, L. L., Lo, K., Chandrasekhar, Y., Reas, R., Yang, J., Eide, D., Funk, K., Kinney, R., Liu, Z., and Merrill, W. (2020) · 2004
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The PDBbind database: Collection of binding affinities for protein-ligand complexes with known three-dimensional structures
Wang, R., Fang, X., Lu, Y., and Wang, S. (2004) · 2004
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Open access institutional and news media tweet dataset for COVID-19 social science research
Yu, J. (2020) · 2004
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Regularization and variable selection via the elastic net
Zou, H., and Hastie, T. (2005) · 2005
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BindingDB: A web-accessible database of experimentally determined protein–ligand binding affinities
Liu, T., Lin, Y., Wen, X., Jorissen, R. N., and Gilson, M. K. (2007) · 2007
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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Modeling the spatial spread of infectious diseases: the global epidemic and mobility computational model
Balcan, D., Gonçalves, B., Hu, H., Ramasco, J., Colizza, V., and Vespignani, A. (2010) · 2010
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Image analysis for understanding embryo development: A bridge from microscopy to biological insights
Luengo-Oroz, M. A., Ledesma-Carbayo, M. J., Peyriéras, N., and Santos, A. (2011) · 2011
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Flower pollination algorithm for global optimization
Yang, X. (2012) · 2012
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An IDEA for short term outbreak projection: Nearcasting using the basic reproduction number
Fisman, D. N., Hauck, T. S., Tuite, A. R., and Greer, A. L. (2013) · 2013
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014) · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo
Homan, M. D., and Gelman, A. (2014) · 2014
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Conditional generative adversarial nets
Mirza, M., and Osindero, S. (2014) · 2014
Cited alongside, same era.
Pharmacophore modeling: Advances, limitations, and current utility in drug discovery
Qing, X., Lee, X. Y., De Raeymaecker, J., Tame, J. R., Zhang, K. Y., De Maeyer, M., and Voet, A. (2014) · 2014
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ChEMBL web services: Streamlining access to drug discovery data and utilities
Davies, M., Nowotka, M., Papadatos, G., Dedman, N., Gaulton, A., Atkinson, F., Bellis, L., and Overington, J. P. (2015) · 2015
Cited alongside, same era.
Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study
Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Hu, S., Wang, Y., Hu, X., Zheng, B., et al. (2020c) · 2020
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COVID-19 dataset clearinghouse
COVID, U. A. (2020) · 2020
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Dimensions COVID-19 publications, datasets and clinical trials
Dimensions AI (2020) · 2020
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An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., and Gardner, L. (2020) · 2020
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Sensitivity of chest CT for COVID-19: Comparison to RT-PCR
Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., and Ji, W. (2020) · 2020
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Potential T-cell and B-cell epitopes of 2019-nCoV
Fast, E., Altman, R. B., and Chen, B. (2020) · 2020
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
Cited alongside, same era.
ZINC 15 – Ligand discovery for everyone
Sterling, T., and Irwin, J. J. (2015) · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015) · 2015
Cited alongside, same era.
Accurate estimation of influenza epidemics using Google search data via ARGO
Yang, S., Santillana, M., and Kou, S. C. (2015) · 2015
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Chen, T., and Guestrin, C. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
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A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics
Feng, C., Huang, Z., Wang, L., Chen, X., Zhai, Y., Zhu, F., Chen, H., Wang, Y., Su, X., Huang, S., et al. (2020) · 2020
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Coronavirus (COVID-19) television coverage
GDELT Project (2020) · 2020
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A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19
Ge, Y., Tian, T., Huang, S., Wan, F., Li, J., Li, S., Yang, H., Hong, L., Wu, N., and Yuan, E. (2020) · 2020
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Targeting COVID-19: GHDDI info sharing portal
GHDDI (2020) · 2020
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Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses
Gussow, A. B., Auslander, N., Wolf, Y. I., and Koonin, E. V. (2020) · 2020
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Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement
Heo, L., and Feig, M. (2020) · 2020
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SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor
Hoffmann, M., Kleine-Weber, H., Schroeder, S., Krüger, N., Herrler, T., Erichsen, S., Schiergens, T. S., Herrler, G., Wu, N.-H., Nitsche, A., et al. (2020) · 2020
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A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories
Hong, L., Lin, J., Li, S., Wan, F., Yang, H., Jiang, T., Zhao, D., and Zeng, J. (2020) · 2020
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Multiple-input deep convolutional neural network model for COVID-19 forecasting in China
Huang, C.-J., Chen, Y.-H., Ma, Y., and Kuo, P.-H. (2020) · 2020
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COVID-19 pandemic – Humanitarian data exchange
Humanitarian Data Exchange (2020) · 2020
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AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app
Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, S., Ali, K., John, C. N., Hussain, I., and Nabeel, M. (2020) · 2020
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Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity
Jiang, X., Coffee, M., Bari, A., Wang, J., Jiang, X., Huang, J., Shi, J., Dai, J., Cai, J., Zhang, T., et al. (2020) · 2020
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Computational predictions of protein structures associated with COVID-19
Jumper, J., Tunyasuvunakool, K., Kohli, P., Hassabis, D., and AlphaFold Team (2020) · 2020
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Essentials for radiologists on COVID-19: an update—radiology scientific expert panel
Kanne, J. P., Little, B. P., Chung, J. H., Elicker, B. M., and Ketai, L. H. (2020) · 2020
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Accurate identification of SARS-CoV-2 from viral genome sequences using deep learning
Lopez-Rincon, A., Tonda, A., Mendoza-Maldonado, L., Claassen, E., Garssen, J., and Kraneveld, A. D. (2020) · 2020
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Considerations, good practices, risks and pitfalls in developing AI solutions against COVID-19
Luccioni, A., Bullock, J., Hoffmann Pham, K., Lam, C. S. N., and Luengo-Oroz, M. (2020) · 2020
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Artificial intelligence cooperation to support the global response to COVID-19
Luengo-Oroz, M., Pham, K. H., Bullock, J., Kirkpatrick, R., Luccioni, A., Rubel, S., Wachholz, C., Chakchouk, M., Biggs, P., Nguyen, T., Purnat, T., and Mariano, B. (2020) · 2020
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CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design
Metsky, H. C., Freije, C. A., Kosoko-Thoroddsen, T.-S. F., Sabeti, P. C., and Myhrvold, C. (2020) · 2020
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Archived COVID-19 related, news, academic articles, essays
Mezei, K. (2020) · 2020
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Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020
Mizumoto, K., Kagaya, K., Zarebski, A., and Chowell, G. (2020) · 2020
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COVID-19 on the chest radiograph: A multi-reader evaluation of an AI system
Murphy, K., Smits, H., Knoops, A. J., Korst, M. B., Samson, T., Scholten, E. T., Schalekamp, S., Schaefer-Prokop, C. M., Philipsen, R. H., Meijers, A., et al. (2020) · 2020
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Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies
Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P., Collins, G. S., and Maruthappu, M. (2020) · 2020
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COVID-19 (coronavirus) survival calculator
Nexoid (2020) · 2020
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Imaging profile of the COVID-19 infection: Radiologic findings and literature review
Ng, M.-Y., Lee, E. Y., Yang, J., Yang, F., Li, X., Wang, H., Lui, M. M.-s., Lo, C. S.-Y., Leung, B., Khong, P.-L., et al. (2020) · 2020
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Potentially highly potent drugs for 2019-nCoV
Nguyen, D. D., Gao, K., Chen, J., Wang, R., and Wei, G. (2020a) · 2020
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Machine intelligence design of 2019-nCoV drugs
Nguyen, D. D., Gao, K., Wang, R., and Wei, G. (2020b) · 2020
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COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning
Ong, E., Wong, M. U., Huffman, A., and He, Y. (2020) · 2020
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COVID-19 hospital impact model for epidemics (CHIME)
Penn Medicine (2020) · 2020
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COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown
Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., and Tizzoni, M. (2020) · 2020
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Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study
Qi, X., Jiang, Z., Yu, Q., Shao, C., Zhang, H., Yue, H., Ma, B., Wang, Y., Liu, C., Meng, X., et al. (2020) · 2020
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Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: A population-based study
Radin, J. M., Wineinger, N. E., Topol, E. J., and Steinhubl, S. R. (2020) · 2020
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Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
Randhawa, G. S., Soltysiak, M. P., El Roz, H., de Souza, C. P., Hill, K. A., and Kari, L. (2020) · 2020
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Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey in the populations when cities/towns are under quarantine
Rao, A. S. S., and Vazquez, J. A. (2020) · 2020
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A prototype model of georeferencing the inherent risk of contagion from COVID-19
Ronsivalle, G. B., Foresti, L., and Poledda, G. (2020) · 2020
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The impact of Coronavirus (COVID-19) on foot traffic
Safegraph (2020) · 2020
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Deep learning-based quantitative computed tomography model in predicting the severity of COVID-19: A retrospective study in 196 patients
Shi, W., Peng, X., Liu, T., Cheng, Z., Lu, H., Yang, S., Zhang, J., Li, F., Wang, M., Zhang, X., Gao, Y., Shi, Y., Zhang, Z., and Shan, F. (2020) · 2020
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Validating a widely implemented deterioration index model among hospitalized COVID-19 patients
Singh, K., Valley, T. S., Tang, S., Li, B. Y., Kamran, F., Sjoding, M. W., Wiens, J., Otles, E., Donnelly, J. P., Wei, M. Y., McBride, J. P., Cao, J., Penoza, C., Ayanian, J. Z., and Nallamothu, B. K. (2020) · 2020
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Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Jie, Y., Wang, R., et al. (2020) · 2020
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COVID-19 healthcare system capacity
Su, A., Luo, D., Castro, H., Moos, L., McFarland, M., Emanuele, R., Kassel, S., and Zhuangfang, N. Y. (2020) · 2020
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AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2
Tang, B., He, F., Liu, D., Fang, M., Wu, Z., and Xu, D. (2020a) · 2020
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Rapid identification of potential inhibitors of SARS-CoV-2 main protease by deep docking of 1.3 billion compounds
Ton, A.-T., Gentile, F., Hsing, M., Ban, F., and Cherkasov, A. (2020) · 2020
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United Nations guidance note on addressing and countering COVID-19 related hate speech
United Nations (2020) · 2020
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A deep learning algorithm using CT images to screen for corona virus disease (COVID-19)
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. (2020a) · 2020
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Chest X-ray findings in 636 ambulatory patients with COVID-19 presenting to an urgent care center: A normal chest X-ray is no guarantee
Weinstock, M., Echenique, A., and Russell, J. e. a. (2020) · 2020
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Prediction models for diagnosis and prognosis of COVID-19 infection: Systematic review and critical appraisal
Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · 2020
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Prediction of criticality in patients with severe COVID-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan
Yan, L., Zhang, H.-T., Xiao, Y., Wang, M., Sun, C., Liang, J., Li, S., Zhang, M., Guo, Y., Xiao, Y., et al. (2020) · 2020
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Improved protein structure prediction using predicted interresidue orientations
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., and Baker, D. (2020) · 2020
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a l p h a alpha -Satellite: An AI-driven system and benchmark datasets for dynamic COVID-19 risk assessment in the United States
Ye, Y., Hou, S., Fan, Y., Zhang, Y., Qian, Y., Sun, S., Peng, Q., Ju, M., Song, W., and Loparo, K. (2020) · 2020
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How to fight an infodemic
Zarocostas, J. (2020) · 2020
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