Sebastian J. Vollmer

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 analyzes 236 AI-driven biomarker discovery studies across 147 diseases and 19 therapeutic areas to identify trends and gaps in datasets, methods, and disease-specific biomarkers.

This paper reviews 86 studies on biologically informed neural networks for multi-omics integration, highlighting trends and challenges in terminology, tools, and evaluation methods.

This study benchmarks 35 AI classifiers across 153 cancer datasets to evaluate their performance in molecular subtype classification, identifying optimal data configurations and model types for different omics modalities and cancer types.