📄 Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen healthcare settings

This repository contains the code used for the experiments in the paper:

Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen healthcare settings
by Diane Duroux, Paul P. Meyer, Giovanni Visonà, and Niko Beerenwinkel.

⚙️ Install the dependencies

Clone the repository, unzip OriginalData.zip, and install the necessary dependencies listed in the requirements.txt file

pip install -r requirements.txt

💻 AMR Classifier Training with ResMLP and inference

The following command trains a ResMLP model for AMR classification using the preprocessed DRIAMS data.

📦 Output

In output//_results/, the script generates:

• test_set_seed0.csv
  ➤ Contains predictions: species, sample_id, drug, response, and Prediction.

🛠 Required Arguments

🚀 Example: ResMLP Training on DRIAMS B2018 with Raw Spectra + Morgan Fingerprints

ulimit -Sn 10000  # Optional: increase file descriptor limit if needed

python3 code/ResAMR_classifier.py \
    --driams_long_table ProcessedData/B2018/combined_long_table.csv \
    --spectra_matrix ProcessedData/B2018/rawSpectra_data.npy \
    --sample_embedding_dim 6000 \
    --drugs_df OriginalData/drug_fingerprints_Mol_selfies.csv \
    --fingerprint_class morgan_1024 \
    --fingerprint_size 1024 \
    --split_type specific \
    --split_ids ProcessedData/B2018/data_splits.csv \
    --experiment_group rawMS_MorganFing \
    --experiment_name ResMLP \
    --seed 0 \
    --n_epochs 2 \
    --learning_rate 0.0003 \
    --patience 10 \
    --batch_size 128

💰 Funding

This research was primarily supported by the ETH AI Center.