Supporting data and code for the paper: "Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events"

The following instructions can be run on a *nix machine to reproduce our work:

1. Download and install Python. Our results were produced using Python 3.11.5, and the specific versions of packages in requirements.txt may require it, but you may try with another Python version...

2. Fork the project repo and navigate to it on your local machine. Typing make help gives a complete list of make commands to be run consecutively.

3. Type make all to run all make commands consecutively, though this will take some time. Instead you can run step-by-step:

   • Type make venv to create a virtual environment and download all required python packages.
   • Type make processed to process the raw data for classification. In Makefile, edit split_seed for a different train/test split, or truncate_seed for a different sampling of truncated time points.
   • Cross validated performance analysis can be run for a number of individual methods, e.g. make lstm analyses the LSTM method. Inspect the Makefile, or type make help for more commands.
   • Type make models to train final ensemble models.
   • Type make interpretation to run the interpretation algorithm.
   • Type make test to test models on test sets.

4. Results are saved in the results/ folder. We provide notebooks that provide visualizations and further analysis in notebooks/.