Hierarchical hidden community detection for protein complex prediction

TL;DR

This paper introduces HirHide, a hierarchical hidden community detection method that enhances traditional algorithms to discover complex, multi-scale, and hidden protein communities in PPI networks, improving detection accuracy.

Contribution

HirHide is the first algorithm to detect hierarchical and hidden communities, significantly improving traditional community detection methods for protein complex prediction.

Findings

HirHide outperforms baseline methods in experiments.

It effectively detects hidden and hierarchical protein complexes.

The method enhances traditional community detection accuracy.

Abstract

Motivation: Discovering functional modules in protein-protein interaction (PPI) networks by optimization methods remains a longstanding challenge in biology. Traditional algorithms simply consider strong protein complexes that can be found in the original network by optimizing some metrics, which causes obstacles for the discovery of weak and hidden complexes shielded by stronger complexes. Also, protein complexes are not only in different density but also in a large range of scale, making it extremely difficult to be detected. Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes. Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities. It is the first community detection algorithm that can find a hierarchical structure as well as hidden structure. We compare the performance of three traditional methods with their HirHide versions. Experimental results show that the HirHide methods using traditional methods as the base algorithms achieve better performance, sometimes even significantly outperform the baselines.