Biomedical Informatics at ETH Zurich

DeepSpot: Deep learning model for predicting spatial transcriptomics from H&E histopathology images. Supports spot-level (Visium) and single-cell (Xenium) resolution.

AESTETIK: Convolutional autoencoder for learning spot representations from spatial transcriptomics and morphology data

Multimodal foundation model predicting transcriptome-wide virtual spatial transcriptomics from histology.

Scripts and data for reproducing the results of MetaGraph-MLA

Repository accompanying the WiNN paper, containing reproducible analysis notebooks for the simulation benchmark and public multi-batch dataset, together with code to reproduce benchmarking results, supplementary analyses, and figure/table generation for the manuscript.

R package for metabolomics data correction using a multi-step pipeline

Scalable annotated de Bruijn graphs for DNA indexing, alignment, and assembly

DeepSpot2Cell: Predicting virtual single-cell spatial transcriptomics from H&E images using spot-level supervision