Rainer Spang

fastMHN enables pan-cancer tumor evolution analysis by approximating complex progression models, identifying a progression group linked to STK11 mutations across multiple cancer types.

Harp improves deconvolution accuracy by harmonizing cell reference profiles and overcoming batch effects in bulk gene expression data.

The paper introduces tissueResolver, an algorithm that infers single-cell type information from bulk RNA-seq data, revealing cell-type-specific regulatory differences in DLBCL subtypes.

This study introduces metMHN, a model to infer metastatic progression from cross-sectional cancer genomics data, identifying TP53 and EGFR as key mediators and highlighting copy number alterations in post-seeding adaptation.

This paper extends cancer progression models to account for observation bias by modeling how genetic events affect tumor detection, improving the accuracy of inferred causal interactions.

PriOmics integrates proteomics with other omics data using graphical models to better understand relationships between proteins and their modifications.

This paper introduces Mutual Hazard Networks to model cancer progression from cross-sectional data, finding that IDH1 mutations promote TP53 mutations in glioblastoma.