HiSCF

HiSCF leverages higher-order network structure to cluster biological networks by detecting small network motifs and identifying functional modules.

Key Features:

• Higher-Order Markov Stochastic Process: Implements a higher-order Markov stochastic process to capture complex connectivity patterns beyond pairwise interactions.
• Higher-Order Connectivity via Network Motifs: Incorporates small network motifs to represent and analyze higher-order connectivity in biological networks.
• Functional Module Identification: Identifies functional modules within networks to reveal organizational structure and group biologically related entities.
• Performance Superiority: Comparative analyses reported that HiSCF outperforms several state-of-the-art clustering models in protein complex identification and gene co-expression module detection.
• Insight into Biological Networks: By considering higher-order motifs, enables identification of overlapping protein complexes and facilitates inference of novel signaling pathways.

Scientific Applications:

• Protein Complex Identification: Clusters proteins based on higher-order interactions to elucidate composition and organization of protein complexes.
• Gene Co-expression Module Detection: Detects groups of co-expressed genes to inform gene regulatory network and functional genomics analyses.
• Signaling Pathway Inference: Facilitates inference of novel signaling pathways through analysis of higher-order network motifs and overlapping modules.

Methodology:

Applies higher-order Markov stochastic processes to analyze higher-order connectivity patterns via small network motifs.