BiCoN

BiCoN performs network-constrained biclustering of gene expression data to identify functionally coherent gene modules and stratify patients for elucidation of disease mechanisms.

Key Features:

• Network-constrained biclustering: Restricts biclusters to functionally related genes interconnected in molecular interaction networks.
• Maximization of expression differences: Optimizes to maximize expression differences between two subgroups of patients to highlight distinct molecular signatures.
• Robustness to noise and batch effects: The network constraint increases robustness against noise and batch effects in high-throughput biological data.
• Reproduction and discovery of subtypes: Reproduces known disease subtypes and identifies novel clinically relevant patient subgroups, validated on breast and lung cancer datasets.
• Heuristic optimization strategies: Integrates several heuristic optimization strategies for robust extraction of disease mechanisms.

Scientific Applications:

• Systems biology: Identification of functionally coherent gene modules and elucidation of molecular mechanisms underlying disease.
• Precision medicine: Patient stratification and biomarker identification to inform targeted therapies and personalized treatment plans.
• Oncology research: Subtype discovery and validation in cancer datasets, including breast and lung cancer.

Methodology:

Network-constrained biclustering that integrates molecular interaction networks with network biology and advanced statistical techniques, employing heuristic optimization strategies to maximize expression differences between two patient subgroups.