💻 Software & Environment Setup
Antimicrobial Resistance (AMR)
The computational toolchain and environment used throughout Machine Learning Approaches for Predicting Antimicrobial Resistance (AMR).
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# Recommended conda environmentconda create -n dstc_env python=3.11 -yconda activate dstc_envpip install biopython pandas abricate-py scikit-learnABRicate / RGI
Resistance-gene detection
Resistance-gene detection
Prokka
Bacterial genome annotation
Bacterial genome annotation
SPAdes
Genome assembly
Genome assembly
R / ggplot2
Surveillance analytics & plots
Surveillance analytics & plots
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.
Earn government-registered certification in Machine Learning Approaches for Predicting Antimicrobial Resistance (AMR)
e-Certificate and e-Marksheet issued on successful completion.
