Gene Disease Association

Gene-disease association research aims to identify genetic variants linked to specific diseases, facilitating improved diagnostics, therapeutics, and disease understanding. Current research heavily utilizes machine learning, employing diverse architectures like graph attention networks, kernel-based neural networks, and transformer models to analyze complex genomic and phenotypic data, often integrating information from protein-protein interaction networks and knowledge graphs. These advancements improve the accuracy and efficiency of identifying disease-associated genes, leading to more precise predictions of clinical trial outcomes and the discovery of novel disease mechanisms. Ultimately, this work accelerates the development of personalized medicine and enhances our understanding of complex diseases.