DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung...

Area of Science:

• Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Molecular interaction networks derived from microarray data offer insights into non-small cell lung cancer (NSCLC) pathogenesis.
• Identifying disease modules within these networks is crucial for understanding disease mechanisms.
• Community detection is a key computational approach for identifying such disease modules.

Purpose of the Study:

• To propose a novel community detection method for identifying disease modules in gene co-expression networks.
• To enhance disease module identification by considering both network topology and gene connection strength.
• To validate the effectiveness of the proposed method in identifying biologically relevant modules for NSCLC.

Main Methods:

• A multi-objective optimization genetic algorithm with decomposition (DM-MOGA) was developed for community detection.
• Key innovations include a boundary correction strategy and the use of improved Davies-Bouldin index and clustering coefficient as fitness functions.
• The method was applied to weighted networks, simultaneously evaluating local gene topology and inter-gene connection strength.

Main Results:

• DM-MOGA successfully identified core disease modules from non-small cell lung cancer gene expression datasets.
• The identified modules demonstrated higher effectiveness compared to several existing advanced module identification methods.
• The method's strategies effectively integrated network topology and connection strength for improved module relevance.

Conclusions:

• The DM-MOGA method provides an effective approach for identifying disease-relevant modules by optimizing novel fitness functions.
• It simultaneously considers local gene topology and global connection strength, leading to more accurate module detection.
• Pathway and gene ontology enrichment analyses confirmed the association of identified core modules with lung cancer, validating their biological significance.