ArticlebioRxiv : the preprint server for biology2026
Determining the age of single cells using scMLEAge.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
Funding
Abstract
Aging is a complex biological process marked by a gradual decline in physiological function that contributes to increased vulnerability to disease and mortality. Numerous studies have investigated the cellular and molecular aspects of aging at single-cell resolution, yet the heterogeneity of cellular aging in an individual remains poorly understood. To enhance our ability to study aging at the single cell level, we developed a statistical framework to predict the age of individual cells based on their transcriptomic profiles. Our Bayesian approach estimates the most likely age of a cell given its read counts. We applied the model to data from Tabula Muris Senis and examined organ- and cell-type-specific transcriptomic signatures of aging. Compared with standard regression-based methods, our framework achieved higher predictive accuracy. We show that
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.