Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient

Abstract

Graphical abstract

Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward

“…The last three approaches in this category, namely: Protein Complexes from Coherent Partition (PC2P) [58] , Greedy Clustering Coefficient and its Variants (GCC-v) [57] , and minimum CUt to detect Biclique spanned subgraphs as protein COmplexes (CUBCO) [59] , formalize the protein complexes as biclique spanned subgraphs to include both sparse and dense complexes. As a result, they resolve the issues with community density and size observed in existing approaches.…”

“…GCC-v [57] is a family of greedy algorithms based on the concept of clustering coefficient and line graph. Given a graph , the greedy algorithm determines a score for every node based on the clustering coefficient.…”

“…To facilitate a fair comparison, the approaches are selected based on two criteria, (i) the public availability of executable code and implementations and (ii) that the method does not rely on any additional biological knowledge or data. Therefore, MCL [22] ), MCODE [7] , CFinder [1] , AP [23] , CMC [45] , PEWCC [99] , Prorank+ [30] , and DPC-NADPIN [73] are selected from the node affinity-based category; ClusterOne [55] ), Core&Peel [63] , IMHRC [48] , PC2P [58] , CC [57] , WCC [57] , OCC [57] , OWCC [57] , and CUBCO [59] from the cluster quality-based category; and DPCMNE [53] from network embedding-based category. For the parameter(s)-dependent approaches (see Table 3 ), we used the default parameter values as suggested in the corresponding original studies.…”

“…To summarize the twelve performance measures and to enable its visualizations, two composite scores are defined. The first composite score is given by the sum over MMR, FRM, SEP, ACC, F-measure [55] , [13] , [87] , [57] , [57] , [59] , and the second one is the sum over MMR and F-measure + across different threshold values [49] , [57] , [57] , [59] .…”

“…The number of predicted protein complexes and the number of proteins in each of them can affect the two measurements. In [57] , the distributions of GO semantic similarity of three GO categories are computed and shown for predicted protein complexes across several network clustering algorithms. This investigation suggests that the approaches detecting a smaller number of protein complexes illustrate narrower distributions for GO semantic similarities.…”