HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

With the availability of more and more genome-scale protein-protein interaction (PPI) networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highestk-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

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Heuristic Modularity for Complex Identification in Protein-Protein Interaction Networks

Iraqi Journal of Science ◽

10.24996/ijs.2019.60.8.22 ◽

2019 ◽

pp. 1846-1859

Author(s):

Amenah H. H. Abdulateef ◽

Bara'a A. Attea ◽

Ahmed N. Rashid

Keyword(s):

Protein Interaction ◽

Protein Complexes ◽

Building Blocks ◽

Detection Accuracy ◽

Protein Protein Interaction ◽

Protein Levels ◽

Cellular Processes ◽

Ppi Networks ◽

Protein Protein Interaction Networks ◽

Different Levels

Due to the significant role in understanding cellular processes, the decomposition of Protein-Protein Interaction (PPI) networks into essential building blocks, or complexes, has received much attention for functional bioinformatics research in recent years. One of the well-known bi-clustering descriptors for identifying communities and complexes in complex networks, such as PPI networks, is modularity function. The contribution of this paper is to introduce heuristic optimization models that can collaborate with the modularity function to improve its detection ability. The definitions of the formulated heuristics are based on nodes and different levels of their neighbor properties. The modularity function and the formulated heuristics are then injected into the mechanism of a single objective Evolutionary Algorithm (EA) tailored specifically to tackle the problem, and thus, to identify possible complexes from PPI networks. In the experiments, different overlapping scores are used to evaluate the detection accuracy in both complex and protein levels. According to the evaluation metrics, the results reveal that the introduced heuristics have the ability to harness the accuracy of the existing modularity while identifying protein complexes in the tested PPI networks.

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Identifying Protein Complexes by Combining Network Topology and Biological Characteristics

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.6084 ◽

2016 ◽

Vol 13 (10) ◽

pp. 7666-7675 ◽

Cited By ~ 1

Author(s):

Buwen Cao ◽

Jiawei Luo ◽

Cheng Liang ◽

Shulin Wang

Keyword(s):

Protein Interaction ◽

Network Topology ◽

Protein Complexes ◽

Positive Interactions ◽

Data Sets ◽

Disease Treatment ◽

Weighting Method ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Physical Interactions

Protein–protein interaction (PPI) data derived from biological experiments include many false-positive interactions which are treated equally as other real physical interactions, thereby complicating the detection of real protein complexes from protein–protein interaction (PPI) networks. In this paper, a new weighting method, named as cwMINE (combined weight of module identification in networks), for detecting protein complexes efficiently in protein interaction networks is presented. cwMINE has a good combination between network topology and biological feature, which can solve false positives efficiently of PPI networks and make discovered protein complexes higher quality. In addition, a new expanding rule during the detection process, namely, expanding coefficient, is developed to filter edges with lower weights. The proposed method is compared with several state- of-the-art algorithms in three yeast PPI networks with two benchmark data sets. The experimental results show that the proposed method outperforms the other algorithms in most datasets in terms of the evaluation metrics. We further validate the effectiveness of our method on a human PPI network constructed from the HPRD dataset to identify important disease-related functional modules and provided valuable indications for disease treatment.

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A comprehensive review and evaluation of computational methods for identifying protein complexes from protein–protein interaction networks

Briefings in Bioinformatics ◽

10.1093/bib/bbz085 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1531-1548 ◽

Cited By ~ 6

Author(s):

Zhourun Wu ◽

Qing Liao ◽

Bin Liu

Keyword(s):

Computational Methods ◽

Protein Interaction ◽

State Of The Art ◽

Protein Complexes ◽

Data Sets ◽

Clustering Methods ◽

Protein Protein Interaction ◽

Advantages And Disadvantages ◽

Ppi Networks ◽

Important Field

Abstract Protein complexes are the fundamental units for many cellular processes. Identifying protein complexes accurately is critical for understanding the functions and organizations of cells. With the increment of genome-scale protein–protein interaction (PPI) data for different species, various computational methods focus on identifying protein complexes from PPI networks. In this article, we give a comprehensive and updated review on the state-of-the-art computational methods in the field of protein complex identification, especially focusing on the newly developed approaches. The computational methods are organized into three categories, including cluster-quality-based methods, node-affinity-based methods and ensemble clustering methods. Furthermore, the advantages and disadvantages of different methods are discussed, and then, the performance of 17 state-of-the-art methods is evaluated on two widely used benchmark data sets. Finally, the bottleneck problems and their potential solutions in this important field are discussed.

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