Protein complexes predictions within protein interaction networks using genetic algorithms

Abstract

BackgroundProtein–protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein–protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein–protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks.ResultsIn this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets.ConclusionsOur algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip.

Refine gene functional similarity network based on interaction networks

“…A good prediction result should have higher accuracy, recall and F-measure values. The evaluation metrics about the quality of predicted complex have been discussed in detail [ 50 , 51 ]. In addition, the reference complexes was downloaded from CORUM database [ 52 ].…”

Ginkgolic acid inhibits the growth of renal cell carcinoma cells via inactivation of the EGFR signaling pathway

“…Network pharmacology provides a strategy to investigate complex mechanisms of drug action and to identify potential drug targets (28). In a network pharmacology system, therapy response can be taken into account based on the robustness of complex disease networks in dealing with node attacks (node linking degree) due to inherent diversity and redundant compensation signaling pathways (12,19). The result of network pharmacology is a highly resilient network system with topological interaction (19,29).…”

“…In a network pharmacology system, therapy response can be taken into account based on the robustness of complex disease networks in dealing with node attacks (node linking degree) due to inherent diversity and redundant compensation signaling pathways (12,19). The result of network pharmacology is a highly resilient network system with topological interaction (19,29). Therefore, computational prediction with a PPI network and topology analyses reveals the potential interactions among compounds and multiple signaling transduction molecules underlying a disease phenotype (30).…”

“…Network pharmacology, a novel systems pharmacology model, was established to explore the therapeutic mechanism of individual drugs on the basis of pharmacokinetics synthesis screening, target identification and network analysis (16,17). A network pharmacology-based approach has displayed potential for investigating the therapeutic mechanism of natural agents in a number of malignancies, including lung cancer, breast cancer and hepatocellular carcinoma (18,19).…”

Protein–protein interaction networks and different clustering analysis in Burkitt’s lymphoma

“…Protein interaction networks are usually composed of a large number of high-density protein nodes but also have sparsely connected nodes, which indicates that clustering is the primary tool for data mining in protein interaction networks [6]. However, different clustering algorithms are based on different mathematical models, there are certain limitations [7]; therefore, using a single clustering method to analyze the network will generate large or small errors and impact research results.…”