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Fig. 1 | Alzheimer's Research & Therapy

Fig. 1

From: Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease

Fig. 1

A diagram illustrating a genotype-informed, network methodology and population-based validation for Alzheimer’s therapeutic discovery. a A framework of network-based Bayesian algorithm (see “Material and methods”) for identifying Alzheimer’ disease (AD) risk genes. Specifically, this algorithm integrates multi-omics data and gene networks to infer risk genes from AD GWAS loci. b Network-based drug repurposing by incorporating ARGs and the human interactome network. c Population-based validation to test the drug user’s relationship with AD outcomes. Comparison analyses were conducted to evaluate the predicted drug-AD association based on individual-level longitudinal patient data and the state-of-the-art pharmacoepidemiologic methods (see “Material and methods”). d Network-based mechanistic observation. Experimental validation of network-predicted drug’s proposed mechanism-of-action in human microglial cells. Specifically, target prioritization and drug repurposing were conducted using network models in addition to the Bayesian algorithm. In step 1, we predicted ARGs (AD risk genes) as potential drug targets from GWAS findings using the Bayesian algorithm. In step 2, we prioritized candidate drugs via quantifying network proximity score between drug targets and ARGs under the human protein–protein interactome network models

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