This repository contains the proposed model in the paper https://www.biorxiv.org/content/10.1101/2020.01.23.917286v1. It also contains notebooks and data to reproduce the results from the paper.
We proposed and characterized a novel biomarker of aging and frailty in mice trained from the large set of the most conventional, easily measured blood parameters such as Complete Blood Counts (CBC) from the open-access Mouse Phenome Database (MPD). Instead of postulating the existence of an aging clock associated with any particular subsystem of an aging organism, we assumed that aging arises cooperatively from positive feedback loops spanning across physiological compartments and leading to an organism-level instability of the underlying regulatory network. To analyze the data, we employed a deep artificial neural network including auto-encoder (AE) and auto-regression (AR) components. The AE was used for dimensionality reduction and denoising the data. The AR was used to describe the dynamics of an individual mouse’s health state by means of stochastic evolution of a single organism state variable, the “dynamic frailty index” (dFI), that is the linear combination of the latent AE features and has the meaning of the total number of regulatory abnormalities developed up to the point of the measurement or, more formally, the order parameter associated with the instability. We used neither the chronological age nor the remaining lifespan of the animals while training the model. Nevertheless, dFI fully described aging on the organism level, that is it increased exponentially with age and predicted remaining lifespan. Notably, dFI correlated strongly with multiple hallmarks of aging such as physiological frailty index, indications of physical decline, molecular markers of inflammation and accumulation of senescent cells. The dynamic nature of dFI was demonstrated in mice subjected to aging acceleration by placement on a high-fat diet and aging deceleration by treatment with rapamycin.
- Python >= 3.8, pip => 20.0
- Required python packages are listed in setup.py. Will be installed automatically.
- To run jupyter notebooks, you should be able to connect to a running jupyter server
Use pip install to install a package.
git clone https://github.com/gero-science/mice_dfi
cd mice_dfi
pip install .This study is mostly based on data from the Mouse Phenome Database. To download the dataset used for training a model simply run the following command.
python -m mice_dfi.dataset.downloadThe other datasets used in this study are stored in this repository.
Start a model training with the command. Note, that datasets should be downloaded in prior
python -m mice_dfi.model.train -o dump -c ./src/mice_dfi/model/config/model_resnet.yaml --tbor display command-line argument help.
python -m mice_dfi.model.train --helpFile model_resnet.yaml could be modified for tuning neural network parameters,
such as depth, activation and dropouts.
Notebooks are stored in the notebooks folder. Note, you have to install and run jupyter server by yourself.
- Write missing documentation
- Add learning rate scheduler and loss weights scheduler
The mice_dfi package is licensed under the GNU GENERAL PUBLIC LICENSE Version 3 (GPLv3)