Biology subjects

Lichtarge, S.

Publications and source records attributed to Lichtarge, S..

2 recordsLinked to original sources

bioRxiv2026

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

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 including cell state trajectories across the human lifespan to generate cell states across long timelapses of human aging. MaxToki generalized to unseen trajectories through in-context learning and predicted novel age-modulating targets that were experimentally verified to influence age-related gene programs and functional decline in vivo. MaxToki represents a promising strategy for temporal modeling to accelerate the discovery of interventions for programming therapeutic cellular trajectories.

bioinformatics↗

bioRxiv2023

Active learning of enhancer and silencer regulatory grammar in photoreceptors

Cis-regulatory DNA elements (CRE) are composed of transcription factor (TF) binding sites that direct cell type-specific gene expression. Deep learning is an emerging strategy to model CREs, but the genome offers too few training examples to learn the complex interactions between TF binding sites that govern CRE activities. We address this limitation using active learning to iteratively train models that predict enhancer and silencer activities in the developing mouse retina. Active learning doubled the performance of models trained on genomic data, resulting in models that accurately distinguish between enhancers and silencers composed of the same TF binding sites. The ability of these models to discriminate between functionally non-equivalent binding sites establishes active learning as an effective strategy for modeling regulatory DNA. One sentence summaryModels of regulatory DNA learn critical interactions between transcription factor binding sites with active machine learning.

genomics↗