Fedor Galkin

Longevity Bench: Are SotA LLMs ready for aging research?

The paper introduces LongevityBench, a benchmark to evaluate LLMs' ability to understand aging biology and predict age-related outcomes from biodata.

The End of Aging Clocks: Training Foundation Models to Reason in Aging and Longevity

The paper introduces Longevity-LLM, a foundation model fine-tuned on aging-related data, achieving high performance in age prediction and proteomic profile generation across multiple modalities.

AI-Driven Toolset for IPF and Aging Research Associates Lung Fibrosis with Accelerated Aging

The paper develops an AI-driven toolset to study IPF and aging, showing IPF as a dysregulation of aging processes rather than just accelerated aging.

Biometric Aging Clock for Practical Utility in Clinical Settings

The paper introduces a cost-efficient aging clock using 22 biomarkers for clinical use, predicting chronological age with 7.35-year error and linking to hypertension, cancer, and obesity.

DORA AI Scientist: Multi-agent Virtual Research Team for Scientific Exploration Discovery and Automated Report Generation

DORA AI Scientist automates scientific research tasks and report generation using multi-agent AI systems and LLMs, reducing manuscript preparation time and enabling more focus on discovery.

Precious3GPT: Multimodal Multi-Species Multi-Omics Multi-Tissue Transformer for Aging Research and Drug Discovery

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

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.

Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects

The paper develops a deep learning model to predict human biological age from gut microbiome data and identifies key microbial biomarkers associated with aging.