Physics-based dynamic PCA models in TensorFlow
This is the source repo. for the physDBD Python package. It allows the creation of physics-based machine learning models in TensorFlow for modeling stochastic reaction networks.
Quickstart
Install:
pip install physDBD
See the Quickstart.
See the example notebook in the example folder of the GitHub repo.
Scan the api_ref.
About
This package for TensorFlow implements modeling stochastic reaction networks with a dynamic PCA model. Please see this paper for technical details:
O. K. Ernst, T. Bartol, T. Sejnowski and E. Mjolsness. Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics. arXiv:2109.05053
The original implementation in the paper is written in Mathematica and can be found here. The Python package developed here translates these methods to TensorFlow.
The only current supported probability distribution is the Gaussian distribution defined by PCA; more general Gaussian distributions are a work in progress.
Requirements
TensorFlow 2.5.0 or later. Note: later versions not tested.
Python 3.7.4 or later.
Installation
Either: use pip:
pip install physDBD
Or alternatively, clone this repo. from GitHub and use the provided setup.py:
python setup.py install
API Documentation
See the api_ref.
Example
See the notebook in the example directory in GitHub repo.
Citing
O. K. Ernst, T. Bartol, T. Sejnowski and E. Mjolsness. Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics. arXiv:2109.05053