Machine learning models predict antibiotic resistance spread
October 2021
Cornell University, Ithaca, USA
Phylogenetic distance, shared ecology, and genomic constraints are often cited as key drivers governing horizontal gene transfer (HGT), although their relative contributions are unclear. Here, the researchers apply machine learning algorithms to a curated set of diverse bacterial genomes to tease apart the importance of specific functional traits in recent HGT events. They find that high-probability not-yet detected antibiotic resistance genes transfer events are almost exclusive to human-associated bacteria. This approach is robust at predicting the HGT networks of pathogens, including Acinetobacter baumannii and Escherichia coli, as well as within localized environments, such as an individual’s gut microbiome.
Functions predict horizontal gene transfer and the emergence of antibiotic resistance
Ilana Lauren Brito
Added on: 02-23-2022
[1] https://www.science.org/doi/10.1126/sciadv.abj5056[2] https://www.drugtargetreview.com/news/98948/machine-learning-models-predict-antibiotic-resistance-spread/?utm_source=Email+marketing&utm_medium=email&utm_campaign=DTR+-+Industry+Insight+-+Thermo+Fisher+-+Informatics+-+19.11.21&utm_term=New+AI+technology+could+shape+the+future+of+RNA+therapeutics&utm_content=https%3a%2f%2femails.drugtargetreview.com%2frussellpublishinglz%2f&gator_td=Cwthm19KYaLKdDoL1rWudvLnht2Dws%2fbFsgEvlhM3rggV7N67epc45IYoHcBSrz0jchdfUZqCyw%2bWNkCaIelSwsxn0kBQQKvNuj9wL1qIkSwyC3HS2fWrlyxAduZw%2fmqW8sOg874fiDhxhovX5msWtsyFuicujiksLRIO%2f6CPAJtCWEAwrvOj%2fnkLUpBfJU9DsjAGHlwFGZcSEwy167MMRKETFKkPbsrKVbYsyDV%2fUlMx23ciiFuuwqRASOdfOeXT05rXhBh6Dl545N4RkHNdw%3d%3d
