Nico Verbeeck

This study creates a spatial ganglioside atlas for mouse and human brains using mass spectrometry imaging, revealing species-specific distributions and differences between species and brain regions.

This paper introduces MIMA, a deep learning framework for multi-omics integration and batch correction, which effectively removes batch effects and preserves biological information across modalities.

The paper integrates spatial metabolomics and spatial transcriptomics on cancer tissues to identify gene-metabolite correlations.

This paper uses deep learning to improve clustering of mass spectrometry imaging data by transforming ion images into neural ion images, resulting in more consistent and fine-grained spatial clusters.