Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models.

ABSTRACT: The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by provid

SUBMITTER: Gherardini L 

PROVIDER: S-EPMC10724880 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature