PMC:1764415 / 3833-5422 JSONTXT

{"project":"2_test","denotations":[{"id":"17147822-14517352-1693543","span":{"begin":428,"end":430},"obj":"14517352"},{"id":"17147822-12711690-1693544","span":{"begin":532,"end":533},"obj":"12711690"},{"id":"17147822-12525261-1693545","span":{"begin":573,"end":575},"obj":"12525261"},{"id":"17147822-14517352-1693546","span":{"begin":596,"end":598},"obj":"14517352"},{"id":"17147822-15180928-1693547","span":{"begin":826,"end":828},"obj":"15180928"},{"id":"17147822-14705023-1693548","span":{"begin":1194,"end":1195},"obj":"14705023"},{"id":"17147822-14566057-1693549","span":{"begin":1387,"end":1389},"obj":"14566057"}],"text":"PPI adjacency matrices can be represented as graphs whose nodes represent proteins and edges represent interactions. The clustering of a PPI dataset can be thereby reduced to graph theoretical problems. But, the binary nature of the current PPI data sets imposes challenges in clustering using conventional approaches. In the maximal clique approach, clustering is reduced to identifying fully connected subgraphs in the graph [11]. To overcome the relatively high stringency imposed by the maximal clique method, the Quasi Clique [7], Molecular Complex Detection (MCODE) [12], Spirin and Mirny [11] algorithms identify densely connected subgraphs rather than fully connected ones by either optimizing an objective density function or using a density threshold. The Restricted Neighborhood Search Clustering Algorithm (RNSC) [13] and Highly Connected Subgraphs (HCS) algorithms [14] harness minimum cost edge cuts for cluster identification. The Markov Cluster Algorithm (MCL) algorithm finds clusters using iterative rounds of expansion and inflation that promote the strongly connected regions and weaken the sparsely connected regions, respectively [15]. The line graph generation approach [9] transforms the network of proteins connected by interactions into a network of connected interactions and then uses the MCL algorithm to cluster the interaction network. Samantha and Liang [16] employed a statistical approach to clustering of proteins based on the premise that a pair of proteins sharing a significantly larger number of common neighbors will have high functional similarity."}