Uncovering Disease-Gene Relationships through Multi-Modal Data Integration and Machine Learning
Date:
Supervisors: Prof. Saman Halgamuge, Dr. Yu Xia, and Dr. Sachith Senevirathne.
Abstract: Identifying genes responsible for complex genetic disorders remains a significant challenge due to the complex nature of polygenic diseases and the demand for a large number of experiments. This study aims to uncover novel disease-gene relationships by integrating multi-modal biological data, such as gene expression, protein-level interactions, and knowledge gathered from research articles through machine learning.
First, we use Developmental and Epileptic Encephalopathy (DEE) as a case study, and further extend it to other neurological disorders to evaluate generalizability. The project aims to establish a scalable framework for identifying novel disease-gene relationships and guiding and accelerating experimental validation.
Confirmation Seminar Presentation
Presenting my 6-months progress for the Advisory committee