Mark A. van de Wiel

The paper analyzes multi-omics data from IDH-mutant astrocytomas to identify molecular markers of malignant progression linked to cell cycling and DNA methylation changes.

The paper develops a computational framework to analyze spatial interactions in tumor microenvironments and shows that spatial context predicts survival in primary central nervous system lymphoma.

Percolate combines gene expression with other genomic data to create robust DNA-based predictors of drug response, outperforming individual data types.

The paper develops TRANSACT, a transfer learning framework for predicting clinical drug response, outperforming existing methods on multiple datasets.

The paper uses RNA-Seq data from 296 family trios to identify genomic imprinting by analyzing allelic expression and parental haplotypes, finding 45 imprinted genes and one novel imprinted transcript.

PRECISE transfers drug response predictors from pre-clinical models to human tumors using domain adaptation, improving biomarker-drug associations in human tumors.