Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases.

ABSTRACT: Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer's Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11488260 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature