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.
 Discovering functional modules in protein-protein interaction networks through optimization remains a longstanding challenge in Biology . Traditional algorithms simply consider strong protein complexes that can be found in the original network by optimizing some metric, which may cause obstacles for the discovery of weak and hidden complexes that are overshadowed by strong complexes. Additionally , protein complexes not only have different densities but also a various range of scales, making them extremely difficult to be detected. We address these issues and propose a hierarchical hidden community detection approach to accurately predict protein complexes of various strengths and scales. We propose a meta-method called HirHide (Hierarchical Hidden Community Detection), which can adopt any standard community detection method as the base algorithm and enable it to discover hierarchical hidden communities as well as boosting the detection on hierarchical strong communities. To our knowledge, this 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.
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Motivation:
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(PPI) networks by optimization methods
networks through optimization
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biology
Biology
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metrics, which causes
metric, which may cause
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shielded by stronger complexes. Also
that are overshadowed by strong complexes. Additionally
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are not only in different density but also in a large range of scale, making it
not only have different densities but also a various range of scales, making them
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Toward this objective, we
We address these issues and
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approach to
detection approach to accurately
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. Results:
of various strengths and scales.
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method
meta-method
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be combined with traditional community detection methods to enable them
adopt any standard community detection method as the base algorithm and enable it
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. It
as well as boosting the detection on hierarchical strong communities. To our knowledge, this
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community detection algorithm that can find a hierarchical structure as well as hidden structure
combination of hierarchical structure with hidden structure, which provides a new perspective for finding protein complexes of various strengths and scales
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three traditional
several standard community detection
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methods using traditional methods as the base algorithms
versions
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,
and