(DRExM³L) Drug REpurposing using and eXplainable Machine Learning and Mechanistic Models of signal transduction"
Project description
Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction
Repository for the drexml
python package: (DRExM³L) Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction
Citation
Find the associated publication here:
Esteban-Medina M, de la Oliva Roque VM, Herráiz-Gil S, Peña-Chilet M, Dopazo J, Loucera C. drexml: A command line tool and Python package for drug repurposing. Computational and Structural Biotechnology Journal 2024;23:1129–43. https://doi.org/10.1016/j.csbj.2024.02.027.
Part of the Intelligent Biology and Medicine special issue:
And the BIB
file:
@article{EstebanMedina2024,
title = {drexml: A command line tool and Python package for drug repurposing},
volume = {23},
ISSN = {2001-0370},
url = {http://dx.doi.org/10.1016/j.csbj.2024.02.027},
DOI = {10.1016/j.csbj.2024.02.027},
journal = {Computational and Structural Biotechnology Journal},
publisher = {Elsevier BV},
author = {Esteban-Medina, Marina and de la Oliva Roque, Víctor Manuel and Herráiz-Gil, Sara and Peña-Chilet, María and Dopazo, Joaquín and Loucera, Carlos},
year = {2024},
month = dec,
pages = {1129–1143}
}
The article was written using drexml
version v1.1.0
. Install it using:
pip install drexml==1.1.0
Version v1.1.1
improves the documentation and README
by including a reference to the published article for easier access.
Setup
To install the drexml
package use the following:
conda create -n drexml python=3.10
conda activate drexml
pip install drexml
If a CUDA~10.2/11.x (< 12) compatible device is available use:
conda create -n drexml --override-channels -c "nvidia/label/cuda-11.8.0" -c conda-forge cuda cuda-nvcc cuda-toolkit gxx=11.2 python=3.10
conda activate drexml
pip install --no-cache-dir --no-binary=shap drexml
To install drexml
in an existing environment, activate it and use:
pip install drexml
Note that by default the setup
will try to compile the CUDA
modules, if not possible it will use the CPU
modules.
Run
To run the program for a disease map that uses circuits from the preprocessed KEGG
pathways and the KDT
standard list, construct an environment file (e.g. disease.env
):
- using the following template if you have a set of seed genes (comma-separated):
seed_genes=2175,2176,2189
- using the following template if you want to use the DisGeNET [1] curated gene-disease associations as seeds.
disease_id="C0015625"
- using the following template if you know which circuits to include (the disease map):
circuits=circuits.tsv.gz
The TSV
file circuits.tsv
has the following format (tab delimited):
index in_disease
P-hsa03320-37 0
P-hsa03320-61 0
P-hsa03320-46 0
P-hsa03320-57 0
P-hsa03320-64 0
P-hsa03320-47 0
P-hsa03320-65 0
P-hsa03320-55 0
P-hsa03320-56 0
P-hsa03320-33 0
P-hsa03320-58 0
P-hsa03320-59 0
P-hsa03320-63 0
P-hsa03320-44 0
P-hsa03320-36 0
P-hsa03320-30 0
P-hsa03320-28 1
where:
index
: Hipathia circuit idin_disease
: (boolean) True/1 if a given circuit is part of the disease
Note that in all cases you can restrict the circuits to the physiological list by setting use_physio=true
in the env
file.
To run the experiment using 10 CPU cores and 0 GPUs, run the following command within an activated environment:
drexml run --n-gpus 0 --n-cpus 10 $DISEASE_PATH
where:
--n-gpus
indicates the number of gpu devices to use in parallel (-1 -> all) (0 -> None)--n-cpus
indicates the number of cpu devices to use in parallel (-1 -> all) 8DISEASE_PATH
indicates the path to the disease env file (e.g./path/to/disease/folder/disease.env
)
Use the --debug
option for testing that everything works using a few iterations.
Note that the first time that the full program is run, it will take longer as it downloads the latest versions of each background dataset from Zenodo:
https://doi.org/10.5281/zenodo.6020480
Contribute to development
The recommended setup is:
- setup
pipx
- setup
miniforge
- use
pipx
to installpdm
- ensure that
pdm
is version >=2.1, otherwise update withpipx
- use
pipx
to inject pdm-bump intopdm
- use
pipx
to installnox
- run
pdm config venv.backend conda
- run
make
, if you want to use a CUDA enabled GPU, usemake gpu=1
- (Recommended): For GPU development, clear the cache using
pdm cache clear
first
Documentation
The documentation can be found here:
https://loucerac.github.io/drexml/
References
[1] Janet Piñero, Juan Manuel Ramírez-Anguita, Josep Saüch-Pitarch, Francesco Ronzano, Emilio Centeno, Ferran Sanz, Laura I Furlong. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucl. Acids Res. (2019) doi:10.1093/nar/gkz1021
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