Journal
Frontiers in Microbiology
Publication Date
2016
Volume
7
Inclusive Pages
1887
Document Type
Open Access Publication
DOI
10.3389/fmicb.2016.01887
Rights and Permissions
Pesesky MW, Hussain T, Wallace M, Patel S, Andleeb S, Burnham C-AD and Dantas G (2016) Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data. Front. Microbiol. 7:1887. doi: 10.3389/fmicb.2016.01887 © 2016 Pesesky, Hussain, Wallace, Patel, Andleeb, Burnham and Dantas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Recommended Citation
Pesesky, Mitchell W.; Hussain, Tahir; Wallace, Meghan; Patel, Sanket; Andleeb, Saadia; Burhnam, Carey-Ann D.; and Dantas, Gautam, "Evaluation of machine learning and rules-based approaches for predicting antimicrobial resistance profiles in gram-negative bacilli from whole genome sequence data." Frontiers in Microbiology. 7, 1887. (2016).
https://digitalcommons.wustl.edu/open_access_pubs/5472
