MCL

MCL clusters nodes in weighted and unweighted networks to identify densely connected regions in biological graphs, enabling detection of protein families and co‑expressed genes.

Key Features:

• Network Topology Utilization: Exploits network connectivity patterns to define clusters based on graph structure.
• Scalability: Operates efficiently on large-scale graphs to accommodate complex biological datasets.
• Support for Weighted and Unweighted Networks: Processes both weighted and unweighted edges to reflect similarity scores or binary relationships in biological data.

Scientific Applications:

• Protein Sequence Similarities: Clusters protein sequence similarity networks to group functionally related proteins and infer potential evolutionary relationships.
• Gene Expression Profile Correlations: Clusters genes with similar expression patterns across conditions or time points to identify co-regulated genes and associated pathways.

Methodology:

Simulates random walks on the graph and iteratively applies expansion and inflation operations to reinforce intra-cluster flow and weaken inter-cluster connections, producing discrete clusters.