Identification of Essential Proteins Based on Edge Clustering Coefficient

Essential proteins are important for the survival and development of organisms. Lots of centrality algorithms based on network topology have been proposed to detect essential proteins and achieve good results. However, most of them only focus on the network topology, but ignore the false positive (FP) interactions in protein–protein interaction (PPI) network. In this paper, gene ontology (GO) information is proposed to measure the reliability of the edges in PPI network and we propose a novel algorithm for identifying essential proteins, named EGC algorithm. EGC algorithm integrates topology character of PPI network and GO information. To validate the performance of EGC algorithm, we use EGC and other nine methods (DC, BC, CC, SC, EC, LAC, NC, PEC and CoEWC) to identify the essential proteins in the two different yeast PPI networks: DIP and MIPS. The results show that EGC is better than the other nine methods, which means adding GO information can help in predicting essential proteins.

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The community detection algorithm based on the node clustering coefficient and the edge clustering coefficient

Proceeding of the 11th World Congress on Intelligent Control and Automation ◽

10.1109/wcica.2014.7053250 ◽

2014 ◽

Cited By ~ 4

Author(s):

Rui Zhang ◽

Lei Li ◽

Chongming Bao ◽

Lihua Zhou ◽

Bing Kong

Keyword(s):

Community Detection ◽

Detection Algorithm ◽

Clustering Coefficient ◽

Node Clustering ◽

Community Detection Algorithm ◽

Edge Clustering Coefficient

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Label propagation algorithm based on edge clustering coefficient for community detection in complex networks

International Journal of Modern Physics B ◽

10.1142/s0217979214502166 ◽

2014 ◽

Vol 28 (30) ◽

pp. 1450216 ◽

Cited By ~ 10

Author(s):

Xian-Kun Zhang ◽

Xue Tian ◽

Ya-Nan Li ◽

Chen Song

Keyword(s):

Complex Networks ◽

Community Detection ◽

Learning Algorithm ◽

Low Complexity ◽

Label Propagation ◽

Clustering Coefficient ◽

Propagation Algorithm ◽

The Stability ◽

Stability And Accuracy ◽

Edge Clustering Coefficient

The label propagation algorithm (LPA) is a graph-based semi-supervised learning algorithm, which can predict the information of unlabeled nodes by a few of labeled nodes. It is a community detection method in the field of complex networks. This algorithm is easy to implement with low complexity and the effect is remarkable. It is widely applied in various fields. However, the randomness of the label propagation leads to the poor robustness of the algorithm, and the classification result is unstable. This paper proposes a LPA based on edge clustering coefficient. The node in the network selects a neighbor node whose edge clustering coefficient is the highest to update the label of node rather than a random neighbor node, so that we can effectively restrain the random spread of the label. The experimental results show that the LPA based on edge clustering coefficient has made improvement in the stability and accuracy of the algorithm.

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Identification of Essential Proteins Based on Improved HITS Algorithm

Genes ◽

10.3390/genes10020177 ◽

2019 ◽

Vol 10 (2) ◽

pp. 177 ◽

Cited By ~ 2

Author(s):

Xiujuan Lei ◽

Siguo Wang ◽

Fang-Xiang Wu

Keyword(s):

Molecular Mechanisms ◽

Clustering Coefficient ◽

New Drugs ◽

Biological Information ◽

Ppi Network ◽

Go Annotation ◽

Detection Techniques ◽

Essential Proteins ◽

Ppi Networks ◽

Topological Characteristics

Essential proteins are critical to the development and survival of cells. Identifying and analyzing essential proteins is vital to understand the molecular mechanisms of living cells and design new drugs. With the development of high-throughput technologies, many protein–protein interaction (PPI) data are available, which facilitates the studies of essential proteins at the network level. Up to now, although various computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a novel method by applying Hyperlink-Induced Topic Search (HITS) on weighted PPI networks to detect essential proteins, named HSEP. First, an original undirected PPI network is transformed into a bidirectional PPI network. Then, both biological information and network topological characteristics are taken into account to weighted PPI networks. Pieces of biological information include gene expression data, Gene Ontology (GO) annotation and subcellular localization. The edge clustering coefficient is represented as network topological characteristics to measure the closeness of two connected nodes. We conducted experiments on two species, namely Saccharomyces cerevisiae and Drosophila melanogaster, and the experimental results show that HSEP outperformed some state-of-the-art essential proteins detection techniques.

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Link prediction based on local community properties

International Journal of Modern Physics B ◽

10.1142/s0217979216502222 ◽

2016 ◽

Vol 30 (31) ◽

pp. 1650222 ◽

Cited By ~ 5

Author(s):

Xu-Hua Yang ◽

Hai-Feng Zhang ◽

Fei Ling ◽

Zhi Cheng ◽

Guo-Qing Weng ◽

...

Keyword(s):

Link Prediction ◽

Local Community ◽

Network Evolution ◽

Clustering Coefficient ◽

Prediction Algorithm ◽

Node Degree ◽

Common Neighbor ◽

Node Similarity ◽

The Common ◽

Edge Clustering Coefficient

The link prediction algorithm is one of the key technologies to reveal the inherent rule of network evolution. This paper proposes a novel link prediction algorithm based on the properties of the local community, which is composed of the common neighbor nodes of any two nodes in the network and the links between these nodes. By referring to the node degree and the condition of assortativity or disassortativity in a network, we comprehensively consider the effect of the shortest path and edge clustering coefficient within the local community on node similarity. We numerically show the proposed method provide good link prediction results.

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