Approved Research

A biologically meaningful and interpretable neural network framework for end-to-end multi phenotype prediction

Lay summary

This project aims to develop new tools to better predict a patient's risk of genetic diseases. The tool is developed using machine learning techniques and makes a disease risk assessment based on the patient's genetic data and existing biological knowledge. Besides providing a risk analysis, the project also aims to understand these risk predictions. By interpreting how and based on what genetic factors the prediction for patients is made, we aim to gain insides in the underlying disease mechanisms and which genetic factors are involved.

The project is part of a larger genome interpretation project carried out by the lab of Yves Moreau. The past decades faster and cheaper techniques to read out DNA have created a large amount of genetic data. This opens the possibility to investigate the complex relations between genetics and diseases by using sophisticated and data-hungry machine learning techniques. Ultimately, this will provide diagnostic tools that help clinicians to assess disease risk more precisely and lead to a deeper understanding of how genetic factors are causing diseases, possibly providing new treatment options.