ArticleBioinformatics (Oxford, England)2024
pyaging: a Python-based compendium of GPU-optimized aging clocks.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Article
- Histological aging signatures for monitoring tissue-specific aging and disease.Nature medicine · 2026Article
- The OmniAge compendium of aging omic biomarkers links mitotic clocks to clonal hematopoiesis and causality.Nature communications · 2026Article
- Epigenetic signatures of cardiometabolic risk in men: accelerated aging and differential methylation replicated across cohorts.Clinical epigenetics · 2026Article
- MethylCurate: Tool for Dataset Curation and Epigenetic Aging Clock Evaluation.bioRxiv : the preprint server for biology · 2026Article
- Cancer Treatment-Related Cardiotoxicity among Survivors of Childhood Cancer: A Comparative and Integrated View of Multiple Measures of Biological Age Acceleration.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Lifestyle factors and DNA methylation-based aging clocks: cross-sectional and longitudinal associations in the Singapore diet and healthy aging cohort.The journal of prevention of Alzheimer's disease · 2026Article
- DeepStrataAge: an interpretable deep-learning clock that reveals stage- and sex-divergent DNA methylation aging dynamics.npj aging · 2026Article
- Towards a personalized perspective on gliomas: an epigenetic-immuno-inflammatory aging framework.Frontiers in immunology · 2026Review
- KoMethylNet: a novel epigenetic clock based on neural network analysis of DNA methylation data and epigenetic age acceleration in a Korean population.BMC medicine · 2025Article
- TranslAGE: A Comprehensive Platform for Systematic Validation of Epigenetic Aging Biomarkers.bioRxiv : the preprint server for biology · 2025Article
- Epigenetic Age Acceleration Mediates Treatment Effects on Cardiometabolic and Cardiovascular Risk in Childhood Cancer Survivors.JACC. CardioOncology · 2025Article
- EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning.International journal of molecular sciences · 2025Article
- Epigenetic ageing clocks: statistical methods and emerging computational challenges.Nature reviews. Genetics · 2025Review
- Histone mark age of human tissues and cell types.Science advances · 2025Article
- Sex-Based Differences in the Association of Epigenetic Age Acceleration with Alzheimer's Disease Biomarkers and Cognitive Measures.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Applicability of epigenetic age models to next-generation methylation arrays.Genome medicine · 2024Article
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1 author.
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Abstract
motivationAging is intricately linked to diseases and mortality. It is reflected in molecular changes across various tissues which can be leveraged for the development of biomarkers of aging using machine learning models, known as aging clocks. Despite advancements in the field, a significant challenge remains: the lack of robust, Python-based software tools for integrating and comparing these diverse models. This gap highlights the need for comprehensive solutions that can handle the complexity and variety of data in aging research.
resultsTo address this gap, I introduce pyaging, a comprehensive open-source Python package designed to facilitate aging research. pyaging harmonizes dozens of aging clocks, covering a range of molecular data types such as DNA methylation, transcriptomics, histone mark ChIP-Seq, and ATAC-Seq. The package is not limited to traditional model types; it features a diverse array, from linear and principal component models to neural networks and automatic relevance determination models. Thanks to a PyTorch-based backend that enables GPU acceleration, pyaging is capable of rapid inference, even when dealing with large datasets and complex models. In addition, the package's support for multi-species analysis extends its utility across various organisms, including humans, various mammals, and Caenorhabditis elegans. AVAILABILITY AND IMPLEMENTATION: pyaging is accessible on GitHub, at https://github.com/rsinghlab/pyaging, and the distribution is available on PyPi, at https://pypi.org/project/pyaging/. The software is also archived on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10335011.
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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 Socratic graph.