New Bayesian Model Tracks Alzheimer's Disease Progression and AT(N) Cascade

Researchers have developed a new Bayesian structural framework called Bayesian Networks with Latent Time Embedding (BN-LTE) to model the progression of Alzheimer's disease. This model estimates disease pseudotime from baseline biomarker data and enforces biologically plausible ordering for the amyloid-tau-neurodegeneration (AT(N)) cascade. BN-LTE was evaluated using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and demonstrated strong spatial reconstruction of tau progression compared to existing forecasting methods. The framework also identified a specific window during disease progression where amyloid sensitivity is most pronounced. AI

RANK_REASON The cluster contains an academic paper detailing a new modeling framework for a specific disease. [lever_c_demoted from research: ic=1 ai=1.0]

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• Alzheimer's disease
• Alzheimer's Disease Neuroimaging Initiative
• AT(N)
• Bayesian Networks with Latent Time Embedding
• Nguyen Linh Dan Le

• paper
• safety

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