Recreation and Enhancement of MSDeepAMR: A Deep Learning Approach for Antimicrobial Resistance Prediction from MALDI-TOF Mass Spectrometry Data

Trained Deep Learning Models for Antimicrobial Resistance Prediction from MALDI-TOF Mass Spectrometry

This archive contains trained neural network models for predicting antibiotic resistance from MALDI-TOF mass spectrometry data across three clinically important bacterial pathogens.

Models Included (~6-7 GB) (Just Download rar files only)

E. coli-Ceftriaxone Models:

• Baseline model (MSDeepAMR architecture)
• Attention-enhanced model (Squeeze-and-Excitation blocks)
• Hyperparameter-optimized model
• 5-model ensemble (seeds: 42, 123, 456, 789, 1024)

K. pneumoniae-Ceftriaxone Models:

• Baseline model - Attention model (paper parameters)
• Species-specific optimized models (dropout 0.35)
• 5-model ensemble

S. aureus-Oxacillin Models:

• Baseline model
• Species-specific optimized models (LR 3×10⁻⁴)
• 5-model ensemble

Model Architecture

• Type: 1D Convolutional Neural Network
• Input: 6,000 m/z bins (2000-20000 Da, 3 Da bin width)
• Framework: TensorFlow 2.19 / Keras
• Format: HDF5 (.h5 files)
• Total Parameters: ~25.2M per model

Performance Results

Usage

1.  Download models from this archive (~6-7 GB)
2. Clone repository:

git clone https://github.com/Muhammad-Lukman/MSDeepAMR_Recreation_Enhancement_project.git 

1. Extract models to models/ folder in repository root
2. Load models in Python: 

from tensorflow import keras model = keras.models.load_model('models/ecoli/5_ensemble/model_seed42.h5')

File Structure

models/
├── ecoli/
│   ├── 1_baseline/final_model_ecoli_ceftriaxone.h5
│   ├── 2_attention/final_attention_model.h5
│   ├── 3_optimized/final_optimized_model.h5
│   └── 5_ensemble/
│       ├── model_seed42.h5
│       ├── model_seed123.h5
│       ├── model_seed456.h5
│       ├── model_seed789.h5
│       └── model_seed1024.h5
├── kpneumoniae/
│   ├── 1_baseline/
│   ├── 2_attention_paper_params/
│   └── 3b_optimized/
│       ├── single_best_model.h5
│       └── ensemble/ (5 models)
└── saureus/
    ├── baseline/
    └── ensemble/ (5 models)

Data Availability

 Preprocessed training data is available in the GitHub repository via Git LFS:

• Repository: MSDeepAMR_Recreation_Enhancement_project
• Path: data/processed/
• Access: git lfs pull after cloning

Raw MALDI-TOF data from DRIAMS database: 

• Source: https://doi.org/10.5061/dryad.bzkh1899q 
• Database: DRIAMS-A (Weis et al., 2022)

Citation

If you use these models, please cite:

This dataset:

Weis, Caroline; Cuénod, Aline; Rieck, Bastian et al. (2025). 
DRIAMS: Database of Resistance Information on Antimicrobials and MALDI-TOF Mass Spectra [Dataset]. 
Dryad. https://doi.org/10.5061/dryad.bzkh1899q

GitHub repository:

@software{muhammadlukman2025msdeepamr,
  author       = {Lukman, Muhammad},
  title        = {Recreation and Enhancement of MSDeepAMR: A Deep Learning Approach for Antimicrobial Resistance Prediction from MALDI-TOF Mass Spectrometry Data},
  year         = 2025,
  url          = {https://github.com/Muhammad-Lukman/MSDeepAMR_Recreation_Enhancement_project}
}

Original MSDeepAMR paper:

@article{lopez2024msdeepamr,
  author       = {López-Cortés, X. A. and Manríquez-Troncoso, J. M. and 
                  Hernández-García, R. and Peralta, D.},
  title        = {MSDeepAMR: antimicrobial resistance prediction based on 
                  deep neural networks and transfer learning},
  journal      = {Frontiers in Microbiology},
  volume       = {15},
  pages        = {1361795},
  year         = {2024},
  doi          = {10.3389/fmicb.2024.1361795}
}

Related Resources

• GitHub Repository:  MSDeepAMR_Recreation_Enhancement_project
• Original Paper: MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning
• DRIAMS Database: DRIAMS: Database of Resistance Information on Antimicrobials and MALDI-TOF Mass Spectra

Technical Requirements

• Python 3.8+
• TensorFlow 2.19.0
• 16GB RAM recommended
• GPU recommended for inference (CPU compatible)

Contact

• Email: dr.mlukmanuaf@gmail.com
• Issues: MSDeepAMR_Recreation_Enhancement_project/issues