Detecting overlapping protein complexes in weighted PPI network based on overlay network chain in quotient space

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Protein complexes are fundamental to biological processes, forming molecular machinery.
• Proteins can participate in multiple complexes, leading to overlap.
• Existing algorithms struggle to accurately detect overlapping protein complexes.

Purpose of the Study:

• To propose a novel algorithm for identifying overlapping protein complexes.
• To address the limitations of current methods in reflecting complex overlap.

Main Methods:

• Developed ONCQS (Overlay Network Chain in Quotient Space) algorithm.
• Utilized maximal complete subgraphs in a multilevel overlay network within quotient space.
• Weighted protein-protein interaction (PPI) networks using Gene Ontology (GO) annotation data.

Main Results:

• ONCQS successfully detects overlapping protein complexes in weighted PPI networks.
• Experiments conducted on four PPI databases (DIP, Gavin, Krogan, MIPS).
• Demonstrated superior performance compared to five state-of-the-art methods.

Conclusions:

• ONCQS significantly improves the identification of overlapping protein complexes.
• The algorithm shows superiority over MCODE, MCL, CORE, ClusterONE, and COACH.
• ONCQS provides a more accurate representation of complex biological organization.