Marc Glettig

The paper expands P-NET, a deep learning framework that uses biological pathways for genomic and transcriptomic predictions, showing competitive performance and interpretability.

The paper evaluates how dataset characteristics affect biologically-informed neural network performance using simulations and empirical testing with prostate cancer data.

H&Enium aligns spatial transcriptomics and H&E imaging data using a contrastive framework to improve cell type classification and gene expression prediction.

This study identifies distinct metastatic phenotypes in cutaneous melanoma using clinico-genomic data and machine learning, linking specific mutations and clinical factors to organ-specific metastasis.

The paper identifies HIF-2α as a therapeutic target for immune-infiltrated melanomas through stratified analysis of clinical and molecular data.

CanSig benchmarks methods for identifying transcriptional signatures in cancer using scRNA-seq data, finding BBKNN as a leading method with clinically relevant outcomes.