• Nguyen, M., Sun, N., Alexander D.C., Feng J., Yeo B.T.T., 2018. Modeling Alzheimer’s disease progression using deep recurrent neural networks, PRNI, 2018.
• Nguyen, M., He T., An L., Alexander D.C., Feng J., Yeo B.T.T., 2019. Predicting Alzheimer’s disease progression using deep recurrent neural networks, under review.

Early identification of people at risk of developing Alzheimer’s disease (AD) would be beneficial for developing treatments. This project uses recurrent neural network (RNN) to predict the progression of AD in subjects over the long term. Temporal interpolation strategies are used to deal with missing data, thus making efficient use of longitudinal data.

Since the whole Github repository is too big, we provide a stand-alone version of only this project and its dependencies. To download this stand-alone repository, visit this link: https://github.com/ThomasYeoLab/Standalone_Nguyen2020_RNNAD

If you want to use the code from our lab's other stable projects (other than Nguyen2020_RNNAD), you would need to download the whole CBIG repository.

• To download the version of the code that was last tested, you can either

  • visit this link: https://github.com/ThomasYeoLab/CBIG/releases/tag/v0.14.0-Nguyen2020_RNNAD

  or

  • run the following command, if you have Git installed

  git checkout -b Nguyen2020_RNNAD v0.14.0-Nguyen2020_RNNAD
  

• This code is compatible with Python 2.7, to create a Python environment similar to what was used for this project:
  1. Install Anaconda with Python 2.7
  2. Create Anaconda environment from our config/CBIG_RNN_python_env.yml file by conda env create -f config/CBIG_RNN_python_env.yml
• An example of how to use the code is detailed in examples/README.md.

• Release v0.14.0 (02/09/2019): Initial release of Nguyen2020_RNNAD project

Please contact Minh Nguyen at [email protected] and Thomas Yeo at [email protected].