ArticleScientific reports2018
Machine learning based classification of cells into chronological stages using single-cell transcriptomics.
Article in Scientific reports, 2018. 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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Who cites it
12 citing papers in PubMed.
- The comparative cell physiology of stem cell-derived and primary human β cells.Nature reviews. Endocrinology · 2026Review
- Cell type-independent timekeeping gene modules enable embryonic stage prediction in zebrafish.bioRxiv : the preprint server for biology · 2025Article
- Metabolomic-based aging clocks.npj metabolic health and disease · 2025Review
- Cholangiocytes contribute to hepatocyte regeneration after partial liver injury during growth spurt in zebrafish.Nature communications · 2025Article
- Optogenetic β cell interrogation in vivo reveals a functional hierarchy directing the CaScience advances · 2024Article
- CellBiAge: Improved single-cell age classification using data binarization.Cell reports · 2023Article
- Biomarkers of aging.Science China. Life sciences · 2023Review
- Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain.Nature aging · 2023Article
- Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.Sensors (Basel, Switzerland) · 2022Review
- Single-cell transcriptome analysis reveals thyrocyte diversity in the zebrafish thyroid gland.EMBO reports · 2020Article
- Genome-scale transcriptional dynamics and environmental biosensing.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy.Scientific reports · 2019Article
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Authors and funding
12 authors.
Funding
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Abstract
Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the classification ability of GERAS to identify molecular factors that are potentially associated with the aging of beta-cells. We show that one of these factors, junba, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling detection of candidate genes associated with aging.
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