This is a re-implementation of XAI-AGE in pytorch, based on the PNET architecture: https://github.com/vanallenlab/pnet.
Biologically informed deep learning for explainable epigenetic clocks
Aging is defined by steady buildup of damage and is a risk factor for chronic diseases. Epigenetic mechanisms like DNA methylation may play a role in organismal aging, but whether they are active drivers or consequences is unknown. Epigenetic clocks, based on DNA methylation, accurately determine a person's biological age. In the past years, a number of accurate epigenetic clocks were developed, and their function and an overview of the field is summarized by Seale et al..
Here we present XAI-AGE, which is a biologically informed, explainable deep neural network model for accurate biological age prediction across many tissues. We show that this approach can identify differentially activated pathways and biological processes from the latent layers of the neural network, and is based on a recently published explainable model used in cancer research, called PNET by Elmarakeby et al..
The research has been published: https://www.nature.com/articles/s41598-023-50495-5 Prosz, A., Pipek, O., Börcsök, J. et al. Biologically informed deep learning for explainable epigenetic clocks. Sci Rep 14, 1306 (2024). https://doi.org/10.1038/s41598-023-50495-5
- Download the remaining files from: Google Drive link: https://drive.google.com/drive/folders/1Geq3l28xagnDpKuI5Ip-OFYLzdY_e5hO?usp=sharing
- Follow the instructions in the Colab notebook
Project Link: https://github.com/Paureel/XAI-AGE-pytorch/
- Elmarakeby H, et al. "Biologically informed deep neural network for prostate cancer classification and discovery." Nature. Online September 22, 2021. DOI: 10.1038/s41586-021-03922-4
- Seale et al. "Making sense of the ageing methylome",Nature Reviews Genetics, DOI:10.1038/s41576-022-00477-6
Funded by the MILAB Artificial Intelligence National Laboratory Program of the Ministry of Innovation and Technology from the National Research, Development and Innovation Fund.