EvodictorDB-AMR is a specialized database associated with the Evodictor software, focusing on antimicrobial resistance (AMR). It is designed to predict the future acquisition of antibiotic resistance genes in pathogenic species through horizontal gene transfers (HGTs). This database leverages the predictive capabilities of Evodictor to mitigate the potential risks of resistance dissemination by identifying which pathogenic species are likely to gain resistance genes in the future.
The query of EvodictorDB-AMR need to be a KEGG Ortholog Identifier. Please annotate KEGG Ortholog Identifier for your query gene by KofamKOALA or KofamScan.
Please just enter the identifier as a query
All of the data and codes provided are freely available licensed under the GNU General Public License v3.0. A manuscript on this database is in preparation. When you use this resource, please cite the following papers for now.:
Naoki Konno, and Wataru Iwasaki. 2023. “Machine Learning Enables Prediction of Metabolic System Evolution in Bacteria.” Science Advances 9 (2): eadc9130.Citation: Konno, N., & Iwasaki, W. (2023). Machine Learning Enables Prediction of Metabolic System Evolution in Bacteria. Science Advances, 9(2), eadc9130. https://doi.org/10.1126/sciadv.adc9130
Contact: Naoki Konno (konno-naoki555[at]g.ecc.u-tokyo.ac.jp) Affiliation: Graduate School of Science, The University of Tokyo