AMR @ ISS

Deep learning-based detection of Antimicrobial Resistance at the International Space Station

This repository contains scripts for the analysis of Antimicrobial Resistance (AMR) on metagenome-assembled genomes and ISS Microbial Tracking-1 (MT-1) datasets.

Publication

https://doi.org/10.1186/s40168-022-01332-w

@article{amriss,	
  doi = {10.1186/s40168-022-01332-w},	
  url = {https://doi.org/10.1186/s40168-022-01332-w},	
  year = 2022,	
  month = {aug},		
  author = {Pedro Madrigal and Nitin K. Singh and Jason M. Wood and Elena Gaudioso and F{\'{e}}lix Hern{\'{a}}ndez-del-Olmo and Christopher E. Mason and Kasthuri Venkateswaran and Afshin Beheshti},	
  title = {Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome},	
  journal = {Microbiome},
  volume = {10},	
  number = {134}
  }

Other project references and information

• Pubmed

• NASA GeneLab publications

• NASA Science Microbiology Program

• The Century of Biology, Space Microbes blog

• TRISH Publications, Translational Research Institute for Space Health

• Aeronautics and space microbiomes article collection

• International Space Station—Microbial Observatory of Pathogenic Viruses, Bacteria, and Fungi (ISS-MOP) Project

• List of Archived ISS Publications 2022

• Video 'Antibiotic resistance in space: Machine learning characterization of bacteria on the ISS':

  • Vimeo
  • YouTube
  • Video Byte (Research Square)