sbinn

KO | EN

Minimalist Open Source Repository Search Engine

gitlite — search

#typescript #ai-agents #deepseek-harness #dsh-plugin #open-source #ai #cli #claude-code #windows #codex #developer-tools #dsh

★ 36 Open GitHub ↗

Systems biology: Identifiability analysis and parameter identification via systems-biology-informed neural networks

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# SBINN: Systems-biology-informed neural network

The source code for the book chapter [M. Daneker, Z. Zhang, G. E. Karniadakis, & L. Lu. Systems biology: Identifiability analysis and parameter identification via systems-biology-informed neural networks. *Computational Modeling of Signaling Networks*, Springer, 87--105, 2023](https://link.springer.com/protocol/10.1007/978-1-0716-3008-2_4).

## Code

- [Structural identifiability analysis](structural_identifiability.ipynb)
- SBINN
    - [Data generation](sbinn/data_generation.py)
    - [TensorFlow code](sbinn/sbinn_tf.py)
    - [PyTorch code](sbinn/sbinn_pytorch.py)
- [Practical identifiability analysis](practical_identifiability.jl)

## Cite this work

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

```
@Inbook{Daneker2023,
  title     = {Systems Biology: Identifiability Analysis and Parameter Identification via Systems-Biology-Informed Neural Networks},
  author    = {Daneker, Mitchell and Zhang, Zhen and Karniadakis, George Em and Lu, Lu},
  editor    = {Nguyen, Lan K.},
  bookTitle = {Computational Modeling of Signaling Networks},
  year      = {2023},
  publisher = {Springer US},
  pages     = {87--105},
  doi       = {10.1007/978-1-0716-3008-2_4}
}
```

## Questions

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

sbinn

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README