The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella
Open accessAbstract
Nontyphoidal Salmonella species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies.
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Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability
Katoh, K., & Standley, D. M. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst010 · Cited by 46,209
BLAST+: architecture and applications
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-421 · Cited by 22,074
FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments
Price, M. N., Dehal, P. S., & Arkin, A. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009490 · Cited by 14,894
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Foodborne illness acquired in the United States—Major pathogens
Scallan, E., Hoekstra, R. M., Angulo, F. J., Tauxe, R. V., Widdowson, M.-A., Roy, S. L., Jones, J. L., & Griffin, P. M. (2011) · Emerging Infectious Diseases · doi:10.3201/eid1701.p11101 · Cited by 7,657
Kumar, A., Roberts, D., Wood, K. E., Light, B., Parrillo, J. E., Sharma, S., Suppes, R., Feinstein, D., Zanotti, S., Taiberg, L., et al. (2006) · Critical Care Medicine · doi:10.1097/01.ccm.0000217961.75225.e9 · Cited by 6,109
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
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“Since reducing the time to optimal antimicrobial therapy significantly improves patient outcomes (22–24), rapid sequencing-based approaches that predict MICs may have clinical utility.”
BackgroundGoldberg, B., Sichtig, H., Geyer, C., Ledeboer, N., & Weinstock, G. M. (2015) · mBio · doi:10.1128/mbio.01888-15 · Cited by 360
Cited by
Florensa, A. F., Kaas, R. S., Clausen, P. T. L. C., Aytan-Aktug, D., & Aarestrup, F. M. (2022) · Microbial Genomics · doi:10.1099/mgen.0.000748 · Cited by 695
Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture
Iskandar, K., Molinier, L., Hallit, S., Sartelli, M., Hardcastle, T. C., Haque, M., Lugova, H., Dhingra, S., Sharma, P., Islam, S., et al. (2021) · Antimicrobial Resistance & Infection Control · doi:10.1186/s13756-021-00931-w · Cited by 479
Standardized bacteriophage purification for personalized phage therapy
Luong, T., Salabarria, A.-C., Edwards, R. A., & Roach, D. R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0346-0 · Cited by 274
Accelerating antibiotic discovery through artificial intelligence
Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C. (2021) · Communications Biology · doi:10.1038/s42003-021-02586-0 · Cited by 234
He, S., Leanse, L. G., & Feng, Y. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.113922 · Cited by 141
Metadata
- Authors
- Nguyen, M., Long, S. W., McDermott, P. F., Olsen, R. J., Olson, R., Stevens, R. L., Tyson, G. H., Zhao, S., & Davis, J. J.
- Venue
- Journal of Clinical Microbiology
- Publisher
- American Society for Microbiology
- Published
- 2019-02
- Volume
- 57
- Issue
- 2
- ISSN
- 0095-1137 (ISSN-L)
- 1098-660X
- OpenAlex
- W2952668074
- Cited by
- 293
- References
- 10
- FWCI
- 24.79
- Type
- journal article
- Language
- en
- Open access
- green
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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.
