SynthesisPloS one2020
RNAAgeCalc: A multi-tissue transcriptional age calculator.
Synthesis in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
What it found
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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.
The trial behind it
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
52 citing papers in PubMed, 81 citations in OpenAlex.
- High-Intensity interval training reduces transcriptomic age: A randomized controlled trial.Aging cell · 2023Trial
- Article
- Tissue-Level Transcriptomic Entropy Reveals Organ-Specific Aging Patterns and Predicts Cancer Progression.Aging cell · 2026Article
- Pasta, a Versatile Transcriptomic Clock, Maps the Chemical and Genetic Determinants of Aging and Rejuvenation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Cellular hallmarks and aging clock of the human lung parenchyma.Nature communications · 2026Article
- BAGE: a Bayesian framework for age prediction based on PBMC gene expression data.BMC bioinformatics · 2026Article
- Article
- Article
- Cellular Senescence of Patient-derived Fibroblasts Reveals the Mid-old Stage as a Critical Window for Transcriptomic Signatures Linked to Alzheimer's Disease Biomarkers and Classification.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2026Article
- Preserved mitochondrial ribosomal protein gene expression marks a youthful transcriptional state in Chinese nonagenarians and centenarians.Cell reports. Medicine · 2026Article
- Region-specific transcriptional signatures of brain aging in the absence of neuropathology at the single-cell level.npj aging · 2026Article
- Review
- ORANGE: a machine learning approach for modeling tissue-specific aging from transcriptomic data.Briefings in bioinformatics · 2026Article
- Immune Ageing Clocks: A Methods-Oriented Review of Tasks, Modalities, Models, and Recalibration.Cells · 2026Review
- Transcriptional signature of induced neurons differentiates virologically suppressed people with HIV from people without HIV.JCI insight · 2026Article
- Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease.NPJ dementia · 2026Article
- Article
- A Machine-Learning Approach Identifies Rejuvenating Interventions in the Human Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Genome-wide methylation biomarkers and biological aging in patients with bipolar disorder characterized for lithium response.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Biological aging reflects decline in physiological functions and is an effective indicator of morbidity and mortality. Numerous epigenetic age calculators are available, however biological aging calculators based on transcription remain scarce. Here, we introduce RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator. By performing a meta-analysis of transcriptional age signature across multi-tissues using the GTEx database, we identify 1,616 common age-related genes, as well as tissue-specific age-related genes. Based on these genes, we develop new across-tissue and tissue-specific age predictors. We show that our transcriptional age calculator outperforms other prior age related gene signatures as indicated by the higher correlation with chronological age as well as lower median and median error. Our results also indicate that both racial and tissue differences are associated with transcriptional age. Furthermore, we demonstrate that the transcriptional age acceleration computed from our within-tissue predictor is significantly correlated with mutation burden, mortality risk and cancer stage in several types of cancer from the TCGA database, and offers complementary information to DNA methylation age. RNAAgeCalc is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#RNAAgeCalc, both as Bioconductor and Python packages, accompanied by a user-friendly interactive Shiny app.
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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.