udemirezen / sbinn

SBINN: Systems-biology informed neural network

SBINN: Systems-biology informed neural network

The source code for the paper M. Daneker, Z. Zhang, G. E. Karniadakis, & L. Lu. Systems biology: Identifiability analysis and parameter identification via systems-biology informed neural networks. arXiv preprint arXiv:2202.01723, 2022.

Code

• Structural identifiability analysis
• SBINN
  • Data generation
  • TensorFlow code
  • PyTorch code
• Practical identifiability analysis

Cite this work

If you use this code for academic research, you are encouraged to cite the following paper:

@article{daneker2022systems,
  title={Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks}, 
  author={Mitchell Daneker and Zhen Zhang and George Em Karniadakis and Lu Lu},
  year={2022},
  eprint={2202.01723},
  archivePrefix={arXiv}
}

Questions

To get help on how to use the data or code, simply open an issue in the GitHub "Issues" section.