Lucas Seninge

The paper develops a spatiotemporal multi-omics platform and machine-learning tool to manufacture hPSC-derived esophageal mucosa.

DREAMIT identifies transcription factor activity patterns along single-cell trajectories, showing improved tissue-specific performance over existing methods.

DNARecords introduces a sparse format for large-scale genomics data to improve analysis efficiency and enable integration with diverse data types for disease risk and drug response prediction.

This paper develops a deep learning framework to improve clinical drug response prediction by integrating gene expression and drug chemical structures, showing better performance than existing methods.

The UCSC Cell Browser allows visualization of gene expression and metadata in single-cell datasets through a web interface and Python package.

This paper introduces VEGA, a variational autoencoder with interpretable latent variables inspired by biological networks, demonstrating its ability to model cellular responses and regulatory mechanisms.