ArticleCell reports2023
CellBiAge: Improved single-cell age classification using data binarization.
Article in Cell reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Article
- Cellular hallmarks and aging clock of the human lung parenchyma.Nature communications · 2026Article
- An snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging.Scientific reports · 2026Article
- A multi-omic atlas in the African turquoise killifish reveals increased glucocorticoid signaling as a hallmark of brain aging.bioRxiv : the preprint server for biology · 2026Article
- Single-cell transcriptomics reveal intrinsic and systemic T cell aging in COVID-19 and HIV.Aging · 2026Article
- Studying ovarian aging and its health impacts: modern tools and approaches.Genes & development · 2025Review
- Facilitate integrated analysis of single cell multiomic data by binarizing gene expression values.Nature communications · 2025Article
- Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.Scientific reports · 2025Article
- Review
- The paradigm shift in neural stem cells basic research driven by artificial intelligence related technologies.Frontiers in cellular neuroscience · 2025Review
- Predicting lung aging using scRNA-Seq data.PLoS computational biology · 2024Article
- Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods.
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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.