Timothy Kassis

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.

The paper develops MECS NPs with controlled surface charge to enhance mucus transport and cell binding for improved mucosal drug delivery.

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.

The paper introduces K-Dense, an AI system that creates accurate transcriptomic aging clocks with uncertainty estimates, showing continuous evolution of aging signatures.

This study evaluates TabPFN's performance on high-dimensional RNA-seq data and finds that adaptations underperform compared to classical methods when using full feature sets.

This paper introduces a human mesofluidic intestinal chip to study transport of drug carriers and bacteria through a live mucosal barrier, revealing mucus dynamics and particle interactions.

This paper introduces Pyxis, a machine learning tool for absolute metabolite quantification in metabolomics, demonstrating its scalability and efficiency compared to traditional methods.