ETD

• Breast Cancer
• Clustering
• Community Detection
• Computational Biology
• ERBB2
• Lung Cancer
• Network Analysis
• Network Biology
• Pattern Mining

This thesis investigates the application of pattern mining and network-based methodologies to gene mutation datasets from patients with ERBB2-altered breast and lung cancers, a cancer subtype for which targeted therapies have recently been developed. The study aimed to identify potential novel driver genes using the FP-Growth algorithm and to model mutational relationships through network analysis. The resulting networks were examined using hub identification, k-means clustering, and community detection techniques. All findings were subsequently validated through survival analyses to evaluate their association with Overall Survival (OS) and Progression-Free Survival (PFS), providing insights into their potential clinical relevance and impact on treatment response.