Abstract
Detecting known protein complexes and predicting undiscovered protein complexes from protein-protein interaction (PPI) networks help us to understand principles of cell organization and its functions. Nevertheless, the discovery of protein complexes based on experiment still needs to be explored. Therefore, computational methods are useful approaches to overcome the experimental limitations. Nevertheless, extraction of protein complexes from PPI network is often nontrivial. Two major constraints are large amount of noise and ignorance of occurrence time of different interactions in PPI network. In this paper, an efficient algorithm, Inter Module Hub Removal Clustering (IMHRC), is developed based on inter-module hub removal in the weighted PPI network which can detect overlapped complexes. By removing some of the inter-module hubs and module hubs, IMHRC eliminates high amount of noise in dataset and implicitly considers different occurrence time of the PPI in network. The performance of the IMHRC was evaluated on several benchmark datasets and results were compared with some of the state-of-the-art models. The protein complexes discovered with the IMHRC method show significantly better agreement with the real complexes than other current methods. Our algorithm provides an accurate and scalable method for detecting and predicting protein complexes from PPI networks.
Highlights
Many biological functions, in living organisms are accomplished by proteins
Biologists generally had concurred that the amount of connections between vertices in a proteinprotein interaction (PPI) network are closely related to the their biological importance, hubs were more likely to be lethal genes[25], whereas later it was found that this correlation might not be completely true[26]
Before presenting the results of our study, we have discussed datasets, evaluation metrics and Gold Standards which were used to assess the results of complex detection algorithms
Summary
Before presenting the results of our study, we have discussed datasets, evaluation metrics and Gold Standards which were used to assess the results of complex detection algorithms. O(p, c) which is called as the matching score, calculates the extent of matching between a reference complex c and a predicted complex p These criteria show the fraction of gold standard complexes which are matched by at least one predicted cluster. Nepusz et al have proposed sum of the Accuracy, MMR and Fraction criterions for comparing the performance of the complex detection algorithms[13]. They showed ClusterONE dominates other complex detection methods, and introduced ClusterONE as a state-of-the-art method. Recently Feng et al have introduced ClusterONE as the state-of-the-art complex detection method and have proposed a new supervised learning method that has achieved a better
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