Barbara Schrörs

This paper introduces GFM, a generative framework that predicts single-cell transcriptomes after genetic perturbations using biological knowledge graphs, outperforming existing methods and demonstrating robustness with limited data.

This study identifies a CD8+ T cell molecular signature specific to small cell lung cancer, showing its prognostic value and potential for immunotherapy.

The paper introduces k4neo, a tool that computes tumor specificity of transcript variants by k-mer indexing of healthy tissue RNA-seq data, identifying tumor-specific antigens across multiple cancer cohorts.

This paper introduces splice2neo, a tool to identify tumor-specific splice junctions from somatic mutations, which can generate neoantigen candidates for immunotherapy.

This study uses multiple instance learning to predict immune checkpoint blockade efficacy based on qualitative neoantigen profiles, showing better performance than quantitative approaches.

The paper analyzes MHC binding and CD8+ T-cell immune escape of SARS-CoV-2 Omicron and Delta variants by comparing epitope binding affinities and identifying mutations affecting immune recognition.

The paper analyzes SARS-CoV-2 genome data to show increasing mutation rates in spike protein variants over time and highlights the need for continuous viral surveillance.