https://github.com/scai-bio/mmpk-sciml
Official implementation of the paper Scientific Machine learning for predicting plasma concentrations in anti-cancer therapy
https://github.com/scai-bio/mmpk-sciml
Last synced: about 2 months ago
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Official implementation of the paper Scientific Machine learning for predicting plasma concentrations in anti-cancer therapy
- Host: GitHub
- URL: https://github.com/scai-bio/mmpk-sciml
- Owner: SCAI-BIO
- License: mit
- Created: 2024-04-09T09:08:15.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-04T09:43:08.000Z (3 months ago)
- Last Synced: 2025-03-04T10:34:36.892Z (3 months ago)
- Language: Python
- Size: 262 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MMPK-SciML
## Description
Official repository for the *Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations* paper . This repository contains the code for our MMPKsciML model and all the models tested in our [paper](https://ascpt.onlinelibrary.wiley.com/doi/full/10.1002/psp4.13313).
## Download
To download everything from this repository onto your local directory, execute the following line on your terminal:
```
$ git clone MMPKSciML
$ cd MMPKSciML
```
## Data
Data can be made available upon reasonable request. Please contact the Department of Clinical Pharmacy at the University of Bonn!
[Prof. Dr. Ulrich Jaehde](mailto:[email protected])
An der Immenburg 4, D-53121 Bonn (Germany)
+49 228 735252
# Repo organization
├── LICENSE
├── README.md
├── data
│ ├── 5fu.csv <- 5FU dataset.
│ ├── Sunitinib <- Sunitinib dataset.
│ └── ... .csv <- csv files
│
├── models <- Trained models. The code generate all the folders as follows:
│ ├── 5FU <- Trained models on the 5FU dataset
│ └──Exp name <- Folder with all the results for a specific experiment
│ └── Sunitinib <- Trained models on the Sunitinib dataset
│ └──Exp name <- Folder with all the results for a specific experiment
│
│
├── 5FU <- Code for all the models using the 5FU dataset
│ ├── MMPKSCIML <- Code for the MMPK-SciML model
│ ├── Classic_ML <- Code for the classic Machine Learning models
│ └── PopPK <- Code for the PopPK model
│
├── Sunitinib <- Code for all the models using the Sunitinib dataset
│ ├── MMPKSCIML <- Code for the MMPK-SciML model
│ ├── Classic_ML <- Code for the classic Machine Learning models
│ └── PopPK <- Code for the PopPK model
## Citation
If this code is helpful in your research, please cite the following papers:
```
@article{valderrama2024integrating,
title={Integrating machine learning with pharmacokinetic models: Benefits of scientific machine learning in adding neural networks components to existing PK models},
author={Valderrama, Diego and Ponce-Bobadilla, Ana Victoria and Mensing, Sven and Fr{\"o}hlich, Holger and Stodtmann, Sven},
journal={CPT: Pharmacometrics \& Systems Pharmacology},
volume={13},
number={1},
pages={41--53},
year={2024},
publisher={Wiley Online Library}
}
```
```
@article{valderrama2024comparing,
title={Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations},
author={Valderrama, Diego and Teplytska, Olga and Koltermann, Luca Marie and Trunz, Elena and Schmulenson, Eduard and Fritsch, Achim and Jaehde, Ulrich and Fr{\"o}hlich, Holger},
journal={CPT: Pharmacometrics \& Systems Pharmacology},
year={2024},
publisher={Wiley Online Library}
}
```
To download everything from this repository onto your local directory, execute the following line on your terminal:
--------
## Contact
- [Prof. Dr. Holger Fröhlich](mailto:[email protected])
- [Diego Valderrama](mailto:[email protected])
- AI and Data Science Group, Bioinformatics Department, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 1, 53757 Sankt Augustin.