Abstract
Developing suitable methods for the detection of protein complexes in protein interaction networks continues to be an intriguing area of research. The importance of this objective originates from the fact that protein complexes are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Up till now, various computational methods were designed for this purpose. However, despite their notable performance, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of protein complexes. In this paper, we present “DyCluster”, a framework to model the dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores. The high accuracy achieved by DyCluster in detecting protein complexes is a valid argument in favor of the proposed method. DyCluster is also able to detect biologically meaningful protein groups. The code and datasets used in the study are downloadable from https://github.com/emhanna/DyCluster.
Highlights
Protein complexes are groups of interacting proteins associated to specific cellular functions [1] and they are fundamental players in almost all biological processes
We here point out: Markov Clustering (MCL) [5] which uses random walks in protein interaction networks; the molecular complex detection (MCODE) algorithm [6] which considers complexes as dense regions grown from highly-weighted vertices; the clustering based on maximal cliques (CMC) method [7]; the Affinity Propagation (AP) algorithm [8]; ClusterONE [9] which identifies protein complexes by clustering with overlapping neighborhood expansion; the restricted neighborhood search (RNSC) algorithm [10, 11]; and CFinder [12] which is based on the clique percolation method
Other approaches which are not centered on the density notion were presented; namely: ProRank [13, 14] and ProRank+ [15] which mainly use a protein ranking algorithm to identify essential proteins in a protein-protein interaction (PPI) network and form complexes ; and PEWCC [16] which assesses the reliability of PPI data based on the weighted clustering coefficient notion prior to detecting protein complexes
Summary
Protein complexes are groups of interacting proteins associated to specific cellular functions [1] and they are fundamental players in almost all biological processes. A large number of these methods are based on the assumption that protein complexes correspond to dense and highlyinterconnected sub-graphs Among those methods, we here point out: Markov Clustering (MCL) [5] which uses random walks in protein interaction networks; the molecular complex detection (MCODE) algorithm [6] which considers complexes as dense regions grown from highly-weighted vertices; the clustering based on maximal cliques (CMC) method [7]; the Affinity Propagation (AP) algorithm [8]; ClusterONE [9] which identifies protein complexes by clustering with overlapping neighborhood expansion; the restricted neighborhood search (RNSC) algorithm [10, 11]; and CFinder [12] which is based on the clique percolation method. Other approaches which are not centered on the density notion were presented; namely: ProRank [13, 14] and ProRank+ [15] which mainly use a protein ranking algorithm to identify essential proteins in a PPI network and form complexes ; and PEWCC [16] which assesses the reliability of PPI data based on the weighted clustering coefficient notion prior to detecting protein complexes
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