Predicting the prevalence of complex genetic diseases from individual genotype profiles using capsule networks
- Linux OS; GPU hardware support
- Python >= v3.7
- PyTorch v1.5.0 (GPU)
- TensorFlow v2.1.0 (GPU)
- sklearn v0.22.2
No need to install the source code. Dependencies can be installed with a few minutes.
One could see pipeline.sh for the general workflow of analysis in this study.
Our proposed DiseaseCapsule:
- capsule.GPU.py
Other approaches for comparison:
- MLP.GPU.py
- CNN.GPU.py
- basicML.py (used for Gene-PCA)
- basicML_allpca.py (used for All-PCA)
- classifier_PRS.py
Potentially diease-related core genes and non-additive genes selection:
- select_core_genes_for_classification.ipynb
- select_nonadditive_genes.py
The ALS data used in this study has been deposited at dbGaP database (Accession: phs003146.v1.p1). Synthetic data can be seen here: https://drive.google.com/open?id=1Mya0YdT4Hf9wUfbcX6y5mubEFWky6Jg-