Kejun Ying

The paper models subcellular aging across 10 organelles using proteomic data from 61,931 individuals and finds that premature organelle aging is associated with increased disease risk and mortality.

This study shows ovarian aging can shift in both directions depending on the systemic environment through inter-organ communication and molecular changes.

This study shows that old white adipose tissue can rejuvenate in young hosts through thermogenic remodeling and pathway activation, reversing biological age markers.

This study identifies splicing degeneration as a hallmark of aging, showing it increases with age and is reversible with calorie restriction or rapamycin.

The paper presents results from an open competition to develop aging biomarkers using DNA methylation data, showing improved predictive models for age, mortality, and multi-morbidity.

This study identifies aging genes in human blood by integrating epigenetic and transcriptomic data, finding genes enriched for immune functions that better replicate across populations and link to aging outcomes.

The paper develops Biolearn, an open-source framework for curating and evaluating aging biomarkers across datasets, finding that harmonization and standardization improve their performance and generalizability.

ECloudGen improves molecular design by using electron clouds as latent variables, generating more potent binders and exploring broader chemical space than existing methods.

This study identifies DIMT-1 as a regulator of C. elegans lifespan and stress resistance through its role in rRNA methylation and germline function.

MethylGPT is a transformer-based model trained on human DNA methylation data that accurately predicts methylation values and improves age and disease prediction.

This paper introduces Ageome, a framework to measure epigenetic age across molecular pathways in mice and humans, revealing pathway-specific aging patterns and heterogeneity in mortality prediction.

The paper shows that imputing DNA methylation loci from epigenome-wide data provides stronger associations with smoking and age than directly measured CpGs.

This paper introduces P3GPT, a multimodal transformer model for aging research and drug discovery that performs multiple tasks across species, tissues, and omics data.

This study identifies transcriptomic biomarkers of mortality across species and models, revealing common aging mechanisms and rejuvenation signatures.

The paper investigates epigenetic aging by comparing single-cell DNA methylation changes across aging and development, revealing stochastic patterns and introducing a new algorithm for analyzing CpG clusters.

The paper uses machine learning on plasma proteome data to develop organ-specific and conventional aging models, showing that age-related diseases reflect accelerated aging of specific organs and systems.

The paper introduces causality-enriched epigenetic clocks, DamAge and AdaptAge, to track detrimental and adaptive methylation changes linked to aging, showing their association with adverse outcomes and beneficial adaptations.

The paper tracks heritable methylation transmission in C. elegans and shows that parental starvation induces intergenerational hormesis via 18S rRNA methylation.

The paper reanalyzes millions of molecular profiles to identify aging interventions using AI agents, finding over 500 interventions that reduce biological age and revealing patterns in aging modulation.

The paper identifies gastrulation in Xenopus laevis as the stage with the lowest epigenetic age and highest developmental disruption, suggesting it as the onset of aging.