AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease.

ABSTRACT: Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer's Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us

SUBMITTER: Raschka T 

PROVIDER: S-EPMC9956604 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature