The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Artificial intelligence and machine learning to fight COVID-19
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A pneumonia outbreak associated with a new coronavirus of probable bat origin
Zhou, P., Yang, X.-L., Wang, X.-G., Hu, B., Zhang, L., Zhang, W., Si, H.-R., Zhu, Y., Li, B., Huang, C.-L., et al. (2020) · Nature · doi:10.1038/s41586-020-2012-7 · Cited by 21,969
Improved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378
Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection?
Fang, L., Karakiulakis, G., & Roth, M. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30116-8 · Cited by 2,911
An interpretable mortality prediction model for COVID-19 patients
Yan, L., Zhang, H.-T., Goncalves, J., Xiao, Y., Wang, M., Guo, Y., Sun, C., Tang, X., Jing, L., Zhang, M., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0180-7 · Cited by 1,301
Randhawa, G. S., Soltysiak, M. P. M., El Roz, H., de Souza, C. P. E., Hill, K. A., & Kari, L. (2020) · PLOS ONE · doi:10.1371/journal.pone.0232391 · Cited by 1,023
Cao, Y., Li, L., Feng, Z., Wan, S., Huang, P., Sun, X., Wen, F., Huang, X., Ning, G., & Wang, W. (2020) · Cell Discovery · doi:10.1038/s41421-020-0147-1 · Cited by 865
Gozes, O., Frid-Adar, M., Greenspan, H. K., Browning, P. D., Zhang, H., Ji, W., Bernheim, A., & Siegel, E. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05037 · Cited by 639
COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning
Ong, E., Wong, M. U., Huffman, A., & He, Y. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01581 · Cited by 499
Ge, Y., Tian, T., Huang, S., Wan, F., Li, J., Li, S., Wang, X., Yang, H., Hong, L., Wu, N., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00568-6 · Cited by 300
CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning designPreprint
Metsky, H. C., Freije, C. A., Kosoko-Thoroddsen, T.-S. F., Sabeti, P. C., & Myhrvold, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.26.967026 · Cited by 162
Cited by
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment
Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., Spada, L. L., Mirmozafari, M., Dehghani, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3001973 · Cited by 551
Coronavirus disease (COVID-19) cases analysis using machine-learning applications
Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B. (2023) · Applied Nanoscience · doi:10.1007/s13204-021-01868-7 · Cited by 348
COVID-19 in early 2021: current status and looking forward
Wang, C., Wang, Z., Wang, G., Lau, J. Y.-N., Zhang, K., & Li, W. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00527-1 · Cited by 339
COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
Pinter, G., Felde, I., Mosavi, A., Ghamisi, P., & Gloaguen, R. (2020) · Mathematics · doi:10.3390/math8060890 · Cited by 328
Improving public services using artificial intelligence: possibilities, pitfalls, governance
Henman, P. (2020) · Asia Pacific Journal of Public Administration · doi:10.1080/23276665.2020.1816188 · Cited by 310
Metadata
- Authors
- Alimadadi, A., Aryal, S., Manandhar, I., Munroe, P. B., Joe, B., & Cheng, X.
- Venue
- Physiological Genomics
- Publisher
- American Physiological Society
- Published
- 2020-04-01
- Volume
- 52
- Issue
- 4
- Pages
- 200-202
- ISSN
- 1094-8341 (ISSN-L)
- 1531-2267
- OpenAlex
- W3013758358
- Cited by
- 608
- References
- 10
- FWCI
- 60.18
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Artificial intelligence and machine learning to fight COVID-19. https://app.cochat.ai/papers/10.1152/physiolgenomics.00029.2020
- The paper
- Alimadadi, A., Aryal, S., Manandhar, I., Munroe, P. B., Joe, B., & Cheng, X. (2020). Artificial intelligence and machine learning to fight COVID-19. Physiological Genomics. https://doi.org/10.1152/physiolgenomics.00029.2020
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
