Andreas Raue

Integration of proteomic data from cell lines and tumors

This paper introduces ProtInt, a deep learning framework for integrating proteomic data from cancer cell lines and tumors, showing improved performance over existing methods.

bulk2scDiff: A Pseudobulk-Conditioned Diffusion Model for Bulk-to-Single-Cell RNASeq Generation

bulk2scDiff generates single-cell RNA-seq data from bulk profiles using a diffusion model, showing good reconstruction of cell populations in cancer datasets.

Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

This paper estimates immune cell content in tumour tissue using single-cell RNA-seq data and demonstrates the importance of tumour-derived reference gene expression profiles for accurate deconvolution.

Predicting Ligand-Dependent Tumors From Multi-Dimensional Signaling Features

The paper predicts ligand-dependent tumor growth in cell lines and patient samples using a mechanistic signaling model and Bagged Decision Trees, finding a correlation between growth factor expression and predicted response.