Temporal AI model predicts drivers of cell state trajectories across human aging

Abstract excerpt

Foundational AI models have recently shown promise for predicting the impact of perturbations on cell states. However, current models typically consider only one cell state at a time, limiting their ability to learn how cellular responses unfold over time, particularly across long trajectories such as diseases of aging. Here, we develop a temporal AI model, MaxToki, trained on nearly 1 trillion gene tokens includi...

Topics

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• Aging
• Algorithms
• Computational Biology
• DNA Methylation
• Epigenesis, Genetic
• Genetic Predisposition to Disease
• Machine Learning
• Prediction Algorithms

Identifiers and source

Literature Corpus work

77e72d82-e2bb-5934-b364-0a7249eec191

DOI

10.64898/2026.03.30.715396