Official code for AdaMedGraph and Personalized Progression Modelling and Prediction in Parkinson's Disease with a Novel Multi-Modal Graph Approach
Description: This folder encompasses all the necessary code for processing the PPMI and PDBP data. Steps: Initially, we processed the motor, non-motor, demographic, and ledd data, adapting code from https://github.com/kseverso/Discovery-of-PD-States-using-ML Next, the processed modalities were combined. PD and HC were selected using "data_merge_hc_pd_separation.ipynb". Feature engineering was performed using either "data_processing_simple.ipynb" or "data_processing_imputation_hyper.ipynb".
Description: This folder contains our AdaMedGraph model. Additionally, "ada_med_graph_pdbp.py" is included to handle multi-dataset situations. You can train and tune the models using "train.py" or define your own training code.
Description: This section includes our comparison models. You have the flexibility to create and train your own models. Usage: Feel free to develop your code and train your models within this section.
Description: This part contains our sub-analysis code. Usage: Utilize the provided code for sub-analyzing the data as per your requirements.