Jean Rene V. Clemenceau

UniST reconstructs dense 3D spatial transcriptomics data from sparse serial sections, restoring structural continuity and biologically meaningful expression patterns in multiple tissue contexts.

S2Omics automatically selects regions of interest from histology images to maximize molecular information content in spatial omics experiments, improving reproducibility and downstream biological discovery.

iSCALE predicts super-resolution gene expression and annotates tissue architecture in large tissues, revealing features missed by conventional spatial transcriptomics.

This paper develops a 3D virtual H&E staining method using holotomography and deep learning for cancer tissues, validated on colon and gastric cancer samples.

This study shows that TMPRSS11B promotes an acidified and immunosuppressive tumor microenvironment in lung squamous cell carcinoma, suggesting it as a therapeutic target.

MSI-SEER uses deep Gaussian processes to predict microsatellite instability from histology images, achieving high accuracy in MSI prediction and ICI response prediction.

The paper uses whole slide images to analyze spatial heterogeneity of tumor mutation burden and immune infiltrates in bladder cancer, finding that low spatial heterogeneity with high TILs correlates with improved survival.

This paper uses deep learning to analyze histopathology images of colorectal cancer to predict progression-free survival by quantifying tumor infiltrating lymphocytes.

This study shows that CRISPR/Cas9-induced INDELs can lead to mRNA misregulation by creating foreign mRNAs or proteins in about half of edited cell lines.

Divine prioritizes disease-causing genes in whole exome sequencing data by integrating phenotype and biological knowledge, outperforming existing methods in a retrospective study.