Hierarchical Hidden Community Detection for Protein Complex Prediction

Abstract BackgroundDiscovering functional modules in protein-protein interaction networks through optimization remains a longstanding challenge in Biology. Traditional algorithms simply consider strong protein complexes found in the original network by optimizing some metric, which may cause obstacles for discovering weak and hidden complexes that are overshadowed by strong complexes. Additionally, protein complexes have not only different densities but also various ranges of scales, making them extremely difficult to be detected. We address these issues and propose a hierarchical hidden community detection approach to predict protein complexes of various strengths and scales accurately. ResultsWe propose a meta-method called HirHide (Hierarchical Hidden Community Detection). It is the first combination of hierarchical structure with hidden structure, which provides a new perspective for finding protein complexes of various strengths and scales. We compare the performance of several standard community detection methods with their HirHide versions. Experimental results show that the HirHide versions achieve better performance and sometimes even significantly outperform the baselines. ConclusionsHirHide can adopt any standard community detection method as the base algorithm and enable it to discover hidden hierarchical communities as well as boosting the detection of strong hierarchical communities. Some biological networks are too complex for standard community detection algorithms to produce a positive performance. Most of the time, a better choice is to choose a corresponding algorithm based on the characteristics of a specific biological network. Under these circumstances, HirHide has clear advantages because of its flexibility. At the same time, according to the natural hierarchy of cells, organelle, intracellular compound etc., hierarchical structure with hidden structure is in line with the characteristics of the data itself, thus helping researchers to study biological interactions more deeply.