Anna Ritz

This paper benchmarks 14 pathway reconstruction algorithms across 822 datasets using a standardized framework to evaluate performance, similarity, and computational efficiency.

ProteinWeaver visualizes protein networks in five model organisms, contextualizing proteins within biological processes using Gene Ontology terms and identifying network motifs and distances.

GRPhIN characterizes graphlets in mixed physical and regulatory interaction networks, revealing conserved and stress-specific patterns across species.

The paper introduces a higher-order community detection method to identify disease modules in protein-protein interaction networks and GWAS data, revealing new disease-associated modules not found by conventional methods.

This paper introduces a method to reconstruct signaling pathways by growing optimal DAGs from protein interaction networks and shows it outperforms existing methods.

This paper quantifies structural differences in signaling pathway databases by analyzing their topological properties and shows that pathways have database-specific characteristics.

The paper introduces PRAUG, a framework that enhances existing pathway reconstruction algorithms, improving their performance across multiple signaling pathways.

This paper develops a machine learning classifier to predict cancer drugs based on gene mutation profiles, achieving 66% accuracy on Breast Invasive Carcinoma data and generalizing to other cancer types.

A RoBERTa-style Transformer, pre-trained on 450K Swiss-Prot sequences in 18 hours on 4 V100 GPUs, rivals DeepFam at family classification and beats PIPR at human protein-protein interaction prediction.

The paper introduces LocPL, a method that improves signaling pathway reconstruction from PPIs by incorporating protein localization data, resulting in more accurate and biologically meaningful reconstructions.

This paper introduces two new distance measures for tumor evolutionary trees, CASet and DISC, which better capture mutation inheritance patterns and show improved performance on simulated and breast cancer datasets.

The paper evaluates connectivity in signaling pathways using different graph representations and introduces a new measure to quantify molecular and pathway influence.