Nurcan Tunçbağ

Extracellular Matrix-Guided Tumor Stratification and Network Models Reveal Clinical Molecular Grades

The paper introduces an ECM-guided patient stratification pipeline using multi-omic data in lung cancer to identify ECM-grades with distinct clinical and molecular features and predict ECM-dependent drug responses.

Unveiling Hidden Connections in Omics Data via pyPARAGON: an Integrative Hybrid Approach for Disease Network Construction

The paper introduces pyPARAGON, a tool for disease network construction using network propagation and graphlets, showing improved accuracy in cancer driver network discovery and patient stratification.

Co-occurring Mutations in Different Genes Can Fuel Oncogenic Signaling and Serve as Metastatic Tumor Markers

The paper identifies co-occurring mutation pairs in cancer genomes that are associated with metastasis and provides a tool for predicting metastatic potential.

Comparative Analysis of Structural Alignment Algorithms for Protein-Protein Interfaces in Template-Based Docking Studies

This study compares four structural alignment algorithms for protein-protein interfaces in template-based docking, finding that TM-align performs well despite not being designed for sequence-order independent alignment.

Structural Coverage of the Human Interactome

The paper assesses the structural coverage of the human interactome by analyzing experimental and modeled protein structures and their alignment to known interactions.

Neurodevelopmental disorders and cancer networks share pathways; but differ in mechanisms, signaling strength, and outcome

This study compares neurodevelopmental disorders and cancer, finding shared pathways but differing in signaling strength and outcomes.

Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact

The paper identifies latent driver double mutations in pan-cancer data and links them to clinical outcomes and drug response.

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

This study analyzes mutation distribution in GBM tumors using 3D spatial organization and network-guided methods, revealing four patient clusters with distinct mutation patterns and pathways.