Inferring genomic signature in Age Macular Degeneration (AMD)

We developed machine learning models (Multinomial Lasso regression, XGboost, Random Forest) to predict distinct stages of age-related macular degeneration (AMD). You can find our final report:

Final_Report.pdf

and presentations used for showcase:

showcase_presentation.pdf

Due to the massive size of the data, datasets needed to run the code are available at:

https://rice.box.com/s/lvuwn3g8e4az5kua2y1qz3drmkgnhq9t Please download the data in folder: D2K_BCM_DATASET

You can import the below packages using the conda command:

conda env create -f environment.yml

This is our Envrionment and Packages that we need

packages:
pandas==0.24.2
seaborn==0.9.0
umap_learn==0.3.10
matplotlib==3.1.0
patsy==0.5.1
numpy==1.18.1
scipy==1.4.1
scikit_learn==0.22.2.post1 py-xgboost-mutex=2.0