Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning
This code accompanies the paper “Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning” by Caroline Weis et al.
This repository is a work in progress. See below for some details about how to reproduce some of the figures of our preprint and stay tuned for more information!
It is recommended to use poetry to
install and interact with the code provided in this repository. This
ensures that all required dependencies are installed correctly. If you
have installed poetry (using your local package manager or the
installation instructions on its official website), the following
commands are sufficient to install everything:
poetry install
poetry shellTo reproduce a part of Figure 3 in the paper (AUROC and AUPRC curves for antimicrobial resistance prediction using logistic regression), it is sufficient to issue the following commands:
poetry shell # Not necessary if you are already in the virtual environment
python plot_fig4_curves_per_species_and_antibiotic_2panels.pyAfterwards, the output file fig4.png will be created, which reproduces
the E. coli panel of Figure 3 in the paper:
You can also call the script with the --help option, i.e. python plot_fig4_curves_per_species_and_antibiotic_2panels.py --help in order
to see which other options are available.
To get a glimpse of the performance of AMR prediction in certain
scenarios, the script collect_results.py can be used. In the absence
of a more complicated matching procedure, the script makes heavy use of
your shell's capabilities to list files. For example, to analyse all
results of all trained classifier for E. coli, use the following
commands:
poetry shell # Not necessary if you are already in the virtual environment
python collect_results.py ../results/fig4_curves_per_species_and_antibiotics/*/*Escherichia*This will result in the following output:
accuracy auprc auroc
mean std mean std mean std
species antibiotic model
Escherichia coli Amoxicillin-Clavulanic acid lightgbm 77.06 0.82 43.83 1.83 67.02 1.41
lr 75.93 0.76 40.96 2.86 65.81 1.41
rf 75.71 0.15 41.13 1.29 66.27 1.76
svm-linear 54.09 10.54 30.84 1.63 56.93 2.08
svm-rbf 62.48 13.59 39.91 1.84 64.23 1.63
Cefepime lightgbm 88.99 0.63 69.85 2.90 88.17 1.47
lr 87.54 0.82 63.18 3.07 85.59 1.22
rf 84.91 0.41 66.99 2.65 86.92 1.75
svm-linear 71.46 19.17 47.35 4.90 76.04 3.49
svm-rbf 58.30 35.40 64.24 1.94 85.24 1.51
Ceftriaxone lightgbm 88.42 0.84 79.41 2.13 89.55 1.36
lr 86.65 0.74 74.38 2.20 87.36 1.26
rf 84.21 0.85 77.01 2.24 87.63 1.52
svm-linear 77.61 2.04 61.04 3.26 79.17 2.31
svm-rbf 83.68 1.70 74.83 2.03 86.81 1.43
Ciprofloxacin lightgbm 82.20 1.02 77.61 1.59 85.32 0.94
lr 79.56 1.14 70.58 2.14 81.00 1.27
rf 77.67 0.74 75.65 1.93 84.25 1.54
svm-linear 67.32 2.13 55.95 3.62 71.40 3.01
svm-rbf 56.58 23.14 67.63 2.98 79.60 2.23
Piperacillin-Tazobactam lightgbm 92.59 0.40 21.12 2.78 71.54 3.90
lr 92.75 0.20 22.01 3.77 71.18 3.54
rf 92.71 0.00 18.46 1.62 69.83 3.10
svm-linear 87.55 0.88 16.41 3.69 66.77 3.32
svm-rbf 38.76 41.48 26.42 5.10 70.77 3.81
Tobramycin lightgbm 87.10 0.70 35.21 3.78 75.05 2.90
lr 87.13 0.64 32.68 3.87 73.14 2.97
rf 86.99 0.00 35.25 3.34 74.12 2.93
svm-linear 73.80 2.95 23.47 3.11 65.06 3.01
svm-rbf 66.29 28.26 33.33 3.66 71.09 2.89
Feel free to experiment with other settings and other scenarios, the script is quite 'smart' and supports different reporting types out of the box.
This code is developed and maintained by members of the Machine Learning and Computational Biology Lab of Prof. Dr. Karsten Borgwardt and the Applied Microbiology Lab of Prof. Dr. Adrian Egli: