Wanqiu Zhang

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.