RETRACTED ARTICLE: Module overlapping structure detection in PPI using an improved link similarity-based Markov clustering algorithm

The identification and analysis of functional modules in protein–protein interaction (PPI) networks provide insight into understanding the organization and function of biological systems. A lot of overlapping structures are shared by the functional modules in PPI networks, which indicates there are...

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Vydáno v:Neural computing & applications Ročník 31; číslo 5; s. 1481 - 1490
Hlavní autoři: Gu, L., Han, Y., Wang, C., Chen, Wei, Jiao, Jun, Yuan, X.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 03.05.2019
Springer Nature B.V
Témata:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
Probability and Statistics in Computer Science
S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing
PPI networks
Link similarity
Markov clustering
Functional modules
ISSN:0941-0643, 1433-3058
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Shrnutí:The identification and analysis of functional modules in protein–protein interaction (PPI) networks provide insight into understanding the organization and function of biological systems. A lot of overlapping structures are shared by the functional modules in PPI networks, which indicates there are some proteins play indispensable roles in different biological processes. Markov clustering (MCL) is a popular algorithm for clustering networks in bioinformatics. In this paper, to identify the overlapping structures among the functional modules and find more modules with biological significance in PPI networks, we propose a Markov clustering algorithm based on link similarity (MLS). First of all, the weighted link similarity is calculated and the link similarity matrix which measures the association strength of the protein interactions can be gotten. Then, the link similarity matrix is divided by applying Markov clustering, and the clustering results are mapped to original networks to analyze the protein modules. The method has been experimented on three databases, including DIP, Gavin and Krogan. Our results show that the MLS cannot only accurately identify the functional modules, but also outperform the original MCL algorithm and the F-measure value improved 5–10% compared with it.
Bibliografie:ObjectType-Correction/Retraction-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-018-3508-z