Official repository for the Scientific Machine learning for predicting plasma concentrations in anti-cancer therapy paper . This repository contains the code for our MMPKsciML model and all the models tested in our preprint.
To download everything from this repository onto your local directory, execute the following line on your terminal:
$ git clone MMPKSciML
$ cd MMPKSciML
Data can be made available upon reasonable request. Please contact the Department of Clinical Pharmacy at the University of Bonn!
An der Immenburg 4, D-53121 Bonn (Germany)
+49 228 735252
├── 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
│ ├── CML <- 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
│ ├── CML <- Code for the classic Machine Learning models
│ └── PopPK <- Code for the PopPK model
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{valderrama2024scientific,
title={Scientific machine learning for predicting plasma concentrations in anti-cancer therapy},
author={Valderrama, Diego and Teplytska, Olga and Koltermann, Luca Marie and Trunz, Elena and Schmulenson, Eduard and Fritsch, Schim and Jaehde, Ulrich and Froehlich, Holger},
journal={medRxiv},
pages={2024--05},
year={2024},
publisher={Cold Spring Harbor Laboratory Press}
}
To download everything from this repository onto your local directory, execute the following line on your terminal:
- Prof. Dr. Holger Fröhlich
- Diego Valderrama
- AI and Data Science Group, Bioinformatics Department, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 1, 53757 Sankt Augustin.