scholarly journals A Central Edge Selection Based Overlapping Community Detection Algorithm for the Detection of Overlapping Structures in Protein–Protein Interaction Networks

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2633 ◽  
Author(s):  
Fang Zhang ◽  
Anjun Ma ◽  
Zhao Wang ◽  
Qin Ma ◽  
Bingqiang Liu ◽  
...  
Keyword(s):  
Community Detection ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Overlapping Community Detection ◽  
Protein Protein Interaction ◽  
Central Node ◽  
Ppi Networks ◽  
Overlapping Community ◽  
Protein Protein Interaction Networks

Overlapping structures of protein–protein interaction networks are very prevalent in different biological processes, which reflect the sharing mechanism to common functional components. The overlapping community detection (OCD) algorithm based on central node selection (CNS) is a traditional and acceptable algorithm for OCD in networks. The main content of CNS is the central node selection and the clustering procedure. However, the original CNS does not consider the influence among the nodes and the importance of the division of the edges in networks. In this paper, an OCD algorithm based on a central edge selection (CES) algorithm for detection of overlapping communities of protein–protein interaction (PPI) networks is proposed. Different from the traditional CNS algorithms for OCD, the proposed algorithm uses community magnetic interference (CMI) to obtain more reasonable central edges in the process of CES, and employs a new distance between the non-central edge and the set of the central edges to divide the non-central edge into the correct cluster during the clustering procedure. In addition, the proposed CES improves the strategy of overlapping nodes pruning (ONP) to make the division more precisely. The experimental results on three benchmark networks and three biological PPI networks of Mus. musculus, Escherichia coli, and Cerevisiae show that the CES algorithm performs well.

scholarly journals GASOLINE: a Cytoscape app for multiple local alignment of PPI networks

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 140 ◽  
Author(s):  
Giovanni Micale ◽  
Andrea Continella ◽  
Alfredo Ferro ◽  
Rosalba Giugno ◽  
Alfredo Pulvirenti
Keyword(s):  
Protein Interaction ◽  
Sampling Strategy ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Local Alignment ◽  
Stochastic Algorithm ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Alignment Task ◽  
Protein Protein Interaction Networks

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithm. GASOLINE starts with the identification of sets of similar nodes, called seeds of the alignment. Alignments are then extended in a greedy manner and finally refined. Both the identification of seeds and the extension of alignments are performed through an iterative Gibbs sampling strategy. GASOLINE is a Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

scholarly journals GASOLINE: a Cytoscape app for multiple local alignment of PPI networks

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 140 ◽  
Author(s):  
Giovanni Micale ◽  
Andrea Continella ◽  
Alfredo Ferro ◽  
Rosalba Giugno ◽  
Alfredo Pulvirenti
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Local Alignment ◽  
Stochastic Algorithms ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Alignment Task ◽  
Protein Protein Interaction Networks ◽  
Go Terms

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithms. To the authors knowledge, it is the first Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

Explore the hidden treasure in protein–protein interaction networks — An iterative model for predicting protein functions

2015 ◽  
Vol 13 (05) ◽  
pp. 1550026 ◽  
Author(s):  
Derui Wang ◽  
Jingyu Hou
Keyword(s):  
Protein Interaction ◽  
Protein Function ◽  
Prediction Performance ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Iterative Approach ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Protein Functions ◽  
Protein Protein Interaction Networks

Protein–protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%–80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.

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