A Granularity-Related Network Refinement Method Based on Module Division and Biological Information for Identifying...

We have condensed the abstract as follows: Essential proteins play a pivotal role in biological systems, and their identification is critical for elucidating complex life mechanisms and disease pathogenesis. Most current methods rely on protein-protein interaction networks (PINs), but raw PINs inherent noise often reduces identification efficiency. Recent studies show protein complexes have distinct fine-grained modular architectures, suggesting systematic consideration of modular granularity during PIN refinement could enhance essential protein identification accuracy. In this paper, we propose a granularity-aware network refinement framework integrating hierarchical module division and multi-source biological evidence. The method involves three steps: (1) Weighted PIN construction through integration of gene expression profiles and Gene Ontology (GO) biological process annotations; (2) Hierarchical module division using the Louvain algorithm to optimize granularity resolution; (3) Critical module detection via synthesis of evolutionary conservation scores and nuclear localization enrichment metrics, ultimately generating a Granularity- Modulated PIN (GM-PIN). To validate our approach, we performed comparative evaluations against four baseline networks (S-PIN, D-PIN, RD-PIN and CM-PIN) using ten state-of-the-art essential protein identification methods (DC, LAC, NC, EC, BC, CC, SC, PR, PeC and WDC) across two species (yeast and human). Experimental results demonstrate that GM-PIN achieves superior performance across multiple metrics, including essential proteins identified in top K, Jackknifing, PRAUC, sensitivity, Fmeasure and accuracy. These findings confirm that our granularity-modulated refinement method effectively constructs high-quality PINs that substantially enhance essential protein discovery.