Mahdi Moqri

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

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.

The paper creates a DNA methylation aging atlas across 17 human tissues, identifying conserved and tissue-specific aging signatures and potential therapeutic targets.

This paper introduces LongReadAge, a probabilistic model for predicting epigenetic age at the cell-type level using long-read sequencing data.

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 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 develops OMICmAge, a multi-omics biomarker of biological age, and shows it outperforms existing markers in predicting chronic diseases and mortality.

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.

The paper develops an in vitro protocol to generate human Pharyngeal Endoderm cells and uses multi-omics analysis to reveal Retinoic Acid's role in their epigenetic and transcriptional development.

The paper identifies PRC2 low-methylated regions as a universal biomarker of aging and rejuvenation, showing they account for most age-related DNA methylation gain across tissues.