Jan-Christian Hütter

The paper introduces PhenoCompass, a model for virtual phenotypic screening that identifies novel PI3K/mTOR inhibitors from a large compound library.

Tangram2 maps cell-to-cell communication by integrating spatial transcriptomics with single-cell RNA sequencing to identify gene expression changes driven by neighboring cell types.

PRESAGE predicts perturbation expression responses using gene embeddings and outperforms existing methods in functional evaluations.

This paper builds and benchmarks perturbative maps of transcriptional and morphological data, demonstrating their utility for discovering new biology and highlighting the importance of standardized frameworks.

This study combines high-throughput screening with deep learning to identify novel antibacterial compounds, achieving a 90X higher hit rate than traditional methods.

This paper introduces SC-VAE, a supervised contrastive framework for analyzing cell perturbation data, showing improved sensitivity in detecting perturbation effects compared to existing methods.

This paper introduces an active learning method to efficiently design Perturb-seq experiments by leveraging multi-modal priors, improving perturbation space exploration with fewer experiments.

The paper introduces a contrastive analysis framework to disentangle perturbation effects from natural cell variation in optical pooled screens, showing improved performance in generating embeddings and reducing technical artifacts.

The paper introduces multiGroupVI, a deep generative model for analyzing grouped scRNA-seq data, and shows it outperforms existing methods on simulated data and identifies regional differences in mouse small intestine data.