Identifying Protein Complexes by Combining Network Topology and Biological Characteristics

2016 ◽  
Vol 13 (10) ◽  
pp. 7666-7675 ◽  
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.

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

2019 ◽  
Vol 21 (5) ◽  
pp. 1531-1548 ◽  
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.

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

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaomin Wang ◽  
Zhengzhi Wang ◽  
Jun Ye
Keyword(s):  
Protein Interaction ◽  
Protein Complexes ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Systematic Analysis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Protein Protein Interaction Networks ◽  
Genome Scale

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.

A METHOD BASED ON LOCAL DENSITY AND RANDOM WALKS FOR COMPLEXES DETECTION IN PROTEIN INTERACTION NETWORKS

2010 ◽  
Vol 08 (supp01) ◽  
pp. 47-62 ◽  
Author(s):  
LIANG YU ◽  
LIN GAO ◽  
KUI LI
Keyword(s):  
Random Walks ◽  
Protein Interaction ◽  
Local Density ◽  
Protein Complexes ◽  
Biological Significance ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Comprehensive Comparison ◽  
Attachment Proteins ◽  
Two Stages

In this paper, we present a method based on local density and random walks (LDRW) for core-attachment complexes detection in protein-protein interaction (PPI) networks whether they are weighted or not. Our LDRW method consists of two stages. Firstly, it finds all the protein-complex cores based on local density of subnetwork. Then it uses random walks with restarts for finding the attachment proteins of each detected core to form complexes. We evaluate the effectiveness of our method using two different yeast PPI networks and validate the biological significance of the predicted protein complexes using known complexes in the Munich Information Center for Protein Sequence (MIPS) and Gene Ontology (GO) databases. We also perform a comprehensive comparison between our method and other existing methods. The results show that our method can find more protein complexes with high biological significance and obtains a significant improvement. Furthermore, our method is able to identify biologically significant overlapped protein complexes.

Heuristic Modularity for Complex Identification in Protein-Protein Interaction Networks

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.

scholarly journals SETS: A Seed-Dense-Expanding Model-Based Topological Structure for the Prediction of Overlapping Protein Complexes

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Soheir Noori ◽  
Nabeel Al-A’araji ◽  
Eman Al-Shamery
Keyword(s):  
Protein Interaction ◽  
Execution Time ◽  
Topological Structure ◽  
Protein Complexes ◽  
Biological Cell ◽  
Ppi Network ◽  
High Similarity ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
F Measure

Defining protein complexes by analysing the protein–protein interaction (PPI) networks is a crucial task in understanding the principles of a biological cell. In the last few decades, researchers have proposed numerous methods to explore the topological structure of a PPI network to detect dense protein complexes. In this paper, the overlapping protein complexes with different densities are predicted within an acceptable execution time using seed expanding model and topological structure of the PPI network (SETS). SETS depend on the relation between the seed and its neighbours. The algorithm was compared with six algorithms on six datasets: five for yeast and one for human. The results showed that SETS outperformed other algorithms in terms of F-measure, coverage rate and the number of complexes that have high similarity with real complexes.

Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data

2013 ◽  
pp. 203-221
Author(s):  
Peter E. Larsen ◽  
Frank Collart ◽  
Yang Dai
Keyword(s):  
Experimental Data ◽  
Protein Interaction ◽  
Network Topology ◽  
Biological Networks ◽  
Dimensional Space ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Transcriptomic Data ◽  
Protein Protein Interaction ◽  
Ppi Networks

The reconstruction of protein-protein interaction (PPI) networks from high-throughput experimental data is one of the most challenging problems in bioinformatics. These biological networks have specific topologies defined by the functional and evolutionary relationships between the proteins and the physical limitations imposed on proteins interacting in the three-dimensional space. In this paper, the authors propose a novel approach for the identification of potential protein-protein interactions based on the integration of known PPI network topology and transcriptomic data. The proposed method, Function Restricted Value Neighborhood (FRV-N), was used to reconstruct PPI networks using an experimental data set consisting of 170 yeast microarray profiles. The results of this analysis demonstrate that incorporating knowledge of interactome topology improves the ability of transcriptome analysis to reconstruct interaction networks with a high degree of biological relevance.

CLUSTERING ALGORITHMS FOR DETECTING FUNCTIONAL MODULES IN PROTEIN INTERACTION NETWORKS

2009 ◽  
Vol 07 (01) ◽  
pp. 217-242 ◽  
Author(s):  
LIN GAO ◽  
PENG-GANG SUN ◽  
JIA SONG
Keyword(s):  
Protein Interaction ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Interaction Network ◽  
Future Research ◽  
Functional Modules ◽  
Sources Of Information ◽  
Protein Protein Interaction ◽  
Metabolic Functions ◽  
Ppi Networks

Protein–Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. When studying the workings of a biological cell, it is useful to be able to detect known and predict still undiscovered protein complexes within the cell's PPI networks. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitate a fast, accurate approach to biological complex identification. Because of its importance in the studies of protein interaction network, there are different models and algorithms in identifying functional modules in PPI networks. In this paper, we review some representative algorithms, focusing on the algorithms underlying the approaches and how the algorithms relate to each other. In particular, a comparison is given based on the property of the algorithms. Since the PPI network is noisy and still incomplete, some methods which consider other additional properties for preprocessing and purifying of PPI data are presented. We also give a discussion about the functional annotation and validation of protein complexes. Finally, new progress and future research directions are discussed from the computational viewpoint.

scholarly journals A Least Square Method Based Model for Identifying Protein Complexes in Protein-Protein Interaction Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Qiguo Dai ◽  
Maozu Guo ◽  
Yingjie Guo ◽  
Xiaoyan Liu ◽  
Yang Liu ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Least Squares Method ◽  
Protein Complexes ◽  
Interaction Network ◽  
Least Square Method ◽  
Least Square ◽  
Great Effort ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks

Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI) network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity.

scholarly journals A New Method for Recognizing Protein Complexes Based on Protein Interaction Networks and GO Terms

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoting Wang ◽  
Nan Zhang ◽  
Yulan Zhao ◽  
Juan Wang
Keyword(s):  
Protein Interaction ◽  
Protein Complexes ◽  
Protein Interaction Networks ◽  
New Method ◽  
Protein Protein Interaction ◽  
Evolution Analysis ◽  
Ppi Networks ◽  
Multiple Protein ◽  
Topology Information ◽  
Go Terms

Motivation: A protein complex is the combination of proteins which interact with each other. Protein–protein interaction (PPI) networks are composed of multiple protein complexes. It is very difficult to recognize protein complexes from PPI data due to the noise of PPI.Results: We proposed a new method, called Topology and Semantic Similarity Network (TSSN), based on topological structure characteristics and biological characteristics to construct the PPI. Experiments show that the TSSN can filter the noise of PPI data. We proposed a new algorithm, called Neighbor Nodes of Proteins (NNP), for recognizing protein complexes by considering their topology information. Experiments show that the algorithm can identify more protein complexes and more accurately. The recognition of protein complexes is vital in research on evolution analysis.Availability and implementation: https://github.com/bioinformatical-code/NNP.

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