Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network.

ABSTRACT: Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein-protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype

SUBMITTER: Park S 

PROVIDER: S-EPMC11370639 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature