Kalin Nonchev

This study benchmarks uncertainty estimation in virtual spatial transcriptomics, finding that predicted means largely explain prediction error, and uncertainty provides limited additional information.

This paper investigates whether histology can improve transcriptomic foundation models by providing cross-modal supervision, but finds limited benefits due to slide-specific structure in gene-expression embeddings.

The paper introduces STMDiT, a diffusion transformer that synthesizes H&E histopathology images from transcriptomic data, improving image quality and transcriptomic fidelity over existing methods.

This paper introduces a framework that aligns spatial transcriptomics data across multiple samples by integrating transcriptomics correction with deep learning, improving integration performance over existing methods.

DeepSpot2Cell predicts virtual single-cell gene expression from H&E images using spot-level supervision, outperforming previous methods on a new benchmark.

SpotWhisperer links histopathology images to spatial gene expression and text annotations using natural language AI, outperforming existing models on a new benchmark dataset.

DeepSpot predicts spatial transcriptomics from H&E images, improving gene correlations across cancer datasets and generating virtual spatial transcriptomics samples.