tberkane / ML-deep-learning-for-Parkinsons-disease-research

Exploring the feasibility of DNN models for the quantitative discrimination between different conformational species of α-synuclein. Project 2 of the CS433 Machine Learning course at EPFL.

Machine Learning Project 2 - Exploring the feasibility of DNN models for the quantitative discrimination between different conformational species of α-synuclein

Project 2 of the CS433-Machine Learning course given at the EPFL Fall 2021.

The team (The Regressionists) is composed by:

• Camillo Nicolò De Sabbata (@cndesabbata)
• Gianluca Radi (@radigianluca)
• Thomas Berkane (@tberkane)

Structure of the repository:

• Project2Classifier.ipynb: notebook containing the code for the classification task
• Project2NN.ipynb: notebook containing the code for the MLP approach to the regression task
• Project2ML.ipynb: notebook containing the code for the ERT approach to the regression task
• helpers.py: some helper functions used in our code
• data: contains the datasets for the 3 types of data: pure oligomer, pure PFF, and mix of oligomer and PFF
• README.md: this file

Before running the jupyter notebooks, please make sure that the paths used for loading the data is consistent with the location in which the .csv files are stored