Marc Claesen

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 uses spatial transcriptomics to analyze the insular cortex of schizophrenia patients and controls, identifying metabolic changes in the blood-brain barrier and parenchyma.

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.