Lucas Paulo de Lima Camillo

An Open Competition for Biomarkers of Aging

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.

Deep Learning-Based Genetic Perturbation Models Do Outperform Uninformative Baselines on Well-Calibrated Metrics

This paper shows deep learning models outperform baselines when using well-calibrated metrics for genetic perturbation prediction.

A single factor for safer cellular rejuvenation

This paper identifies SB000, a single gene factor that rejuvenates cells from multiple germ layers without inducing pluripotency, rivaling the effectiveness of Yamanaka factors.

Single-Cell Analysis of Microglia and Monocyte Dynamics Uncover Distinct TNF-a-driven Neuroimmune Signatures after Intracerebral Hemorrhage

The paper uses single-cell transcriptomics to study microglia and monocyte dynamics after ICH, finding TNF signaling drives early monocyte activation and is linked to better neurological outcomes.

CpGPT: a Foundation Model for DNA Methylation

CpGPT is a foundation model for DNA methylation analysis that outperforms specialized models in aging-related tasks and can impute methylation profiles across diverse conditions.

pyaging: a Python-based compendium of GPU-optimized aging clocks

pyaging provides a Python package with over 30 aging clocks for analyzing molecular data types across species, using GPU-optimized models for efficient inference.

Histone mark age of human tissues and cells

The paper develops histone mark-based age prediction models across tissues, showing comparable accuracy to DNA methylation predictors and identifying key developmental pathways involved in aging.

Autonomous AI Agents Discover Aging Interventions from Millions of Molecular Profiles

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.

AltumAge: A Pan-Tissue DNA-Methylation Epigenetic Clock Based on Deep Learning

AltumAge is a deep learning-based DNA methylation clock that improves age prediction accuracy and interpretability across tissues and ages.