Detection of infection and antimicrobial resistance of bacteria

This repository contains the official python implementation of the paper - https://doi.org/10.1101/2022.07.07.499154

Dataset download link -> Google Drive

Setting up the environment

## create new environment
conda create -n qpm_env python=3.6
source activate qpm_env

## Adding new environment to JupyterLab
conda install -c anaconda ipykernel -y
python -m ipykernel install --user --name=qpm_env

conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c nvidia
conda install -c conda-forge matplotlib
conda install -c conda-forge wandb

#install remaining packages through pip
pip install -r requirements.txt

Directory Structure

• Modules - supporting python library
• Notebooks - different experiments

Classification Task	Training Notebook	Saved Model	N bacteria grouped evaluation
Gram Stain Classification	Notebook	link	Notebook
Antibiotic Resistance Prediction	Notebook	link	Notebook
Species Level Classification	Notebook	link	Notebook
Strain Level Classification	Notebook	link	Notebook

Running predicitons on the blind test

After downloading pretrained models and test dataset, you can edit the path in the python scripts below and the dataloader.py using data_dir variable.

Specify the concentration level (N) to be used for prediction in the line below,

bacteria-classification/notebooks/N_group_evaluation/verify_strain.py

Line 68 in 75ea653

for N in [63,15,7,3,1]:

Eg. running for N = 3, replace the line with, for N in [3]:

• Antibiotic Resistance - verify_arp.py
• Gram Stain Classification - verify_gram.py
• Species Classification - verify_species.py
• Strain Classification - verify_strain.py