This project is related to Huntington's Disease Age at Onset Estimation Improvement
The identification and diagnosis of rare diseases can be challenging due to the limited availability of data and expertise. Despite HD being a rare disease, the Enroll-HD dataset, which is sufficiently large, has enabled machine learning approaches to be used. Machine learning (ML) is a sub-field of Artificial Intelligence that utilises an algorithm, referred to as a model, to process and analyse data. The data is used to train the ML model to make decisions or draw conclusions. After training, predictions can be made based on new data.
The main objective of this research is to improve the estimation of AAO for HD, which currently relies solely on CAG repeat length. Enroll-HD provides high-quality longitudinal data on HD patients, making it a valuable resource for ML algorithms.
The notebooks are shared here. The numbering on the names helps to identify the jobs they have carried out, although they can work independently. The PDS-5 dataset used from Enroll-HD can be requested at https://enroll-hd.org/for-researchers/access-data/