Elon Litman

MetaMuse curates and harmonizes biomedical metadata using a multi-agent AI system, achieving high accuracy and scalability in standardizing unstructured data from repositories like GEO.

This paper introduces a self-supervised foundation model for single-cell transcriptomes that predicts gene sets from context and outperforms baselines in downstream tasks.

Revive-Flow predicts and reverses blood DNA methylation aging using a flow-matching model trained on epigenetic data, achieving high accuracy in rejuvenation.

Melchior is an RNA basecaller using a hybrid Mamba-Transformer model to improve accuracy in homopolymer regions by combining global and local feature modeling.