Complex discovery from weighted PPI networks

Liu, Guimei; Wong, Limsoon; Chua, Hon Nian

doi:10.1093/bioinformatics/btp311

Cited by 430 publications

(402 citation statements)

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“…Liu et al . developed the clustering based on maximal cliques (CMC) method [10], which generates all maximal cliques from the protein-protein interaction networks, and assembles highly overlapped clusters based on their interconnectivity. Wu et al .…”

Section: Introductionmentioning

confidence: 99%

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

et al. 2014

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BackgroundProtein complexes play important roles in biological systems such as gene regulatory networks and metabolic pathways. Most methods for predicting protein complexes try to find protein complexes with size more than three. It, however, is known that protein complexes with smaller sizes occupy a large part of whole complexes for several species. In our previous work, we developed a method with several feature space mappings and the domain composition kernel for prediction of heterodimeric protein complexes, which outperforms existing methods.ResultsWe propose methods for prediction of heterotrimeric protein complexes by extending techniques in the previous work on the basis of the idea that most heterotrimeric protein complexes are not likely to share the same protein with each other. We make use of the discriminant function in support vector machines (SVMs), and design novel feature space mappings for the second phase. As the second classifier, we examine SVMs and relevance vector machines (RVMs). We perform 10-fold cross-validation computational experiments. The results suggest that our proposed two-phase methods and SVM with the extended features outperform the existing method NWE, which was reported to outperform other existing methods such as MCL, MCODE, DPClus, CMC, COACH, RRW, and PPSampler for prediction of heterotrimeric protein complexes.ConclusionsWe propose two-phase prediction methods with the extended features, the domain composition kernel, SVMs and RVMs. The two-phase method with the extended features and the domain composition kernel using SVM as the second classifier is particularly useful for prediction of heterotrimeric protein complexes.

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Section: Introductionmentioning

confidence: 99%

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

et al. 2014

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“…Protein complexes generally correspond to dense subgraphs in a PPI network because proteins in the same complex are highly interactive with one another. Many algorithms, such as Cfinder [2], CMC [3], IPCA [4], MCODE [5], and PCP [6], based on the hypothesis have been proposed to discover protein complexes.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, many computational approaches have been proposed to assess the reliability of high-throughput protein interaction data. Liu et al [3] proposed the AdjustCD weighting method, which applies an iterative procedure that relies solely on network topology to calculate the reliability of a binary protein interaction. Their experimental results show that the iterative scoring method can effectively reduce the effect of random noise on the performance of a complex prediction method.…”

Section: Introductionmentioning

confidence: 99%

“…First, a weighted method for each pair of interacting proteins is proposed on the basis of AdjustCD distance [3] and GO semantic similarity. Protein complexes are then detected on the basis of minimum cut in the weighted network.…”

Section: Introductionmentioning

confidence: 99%

“…Protein complexes are then detected on the basis of minimum cut in the weighted network. We compare our method with CMC [3], Core [10], PCP [6], Cfinder [2], Coach [8], and IPCA [4] in terms of specificity, sensitivity, and f-score by using two datasets of yeast protein interactions, Krogan [15] and DIP [16]. The experimental results show that our approach is more efficient than these algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Protein Complex Identification Algorithm Based on the Integration of Local Network Topology and Gene Ontology

Luo¹,

Wang²

2014

Proceedings of the 3rd International Conference on Computer Science and Service System

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Abstract-The identification of protein complexes is an essential step to understand the principles of cellular organization and biochemical phenomena. A large dataset of experimentally detected protein-protein interactions (PPI) has been determined using high-throughput experimental techniques. However, these datasets usually contain spurious interactions, which complicate the accurate identification of protein complexes by using computational methods. In this study, a novel method is developed to predict protein complexes based on PPI network topology and gene ontology. A protein functional similarity is performed to estimate the reliability of this interaction. A minimum cut-based method is used to detect protein complexes in the weight network. Experimental results show that our method performs better than several efficient, existing clustering algorithms.

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2011

Data Management of Protein Interaction Networks

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Complex discovery from weighted PPI networks

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 430 publications

References 27 publications

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

A Novel Protein Complex Identification Algorithm Based on the Integration of Local Network Topology and Gene Ontology

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