Journal of Algorithms and Computation
Open Access

An attributed network embedding method to predict missing links in protein-protein interaction networks

Document Type : Research Paper

Authors

Ali Golzadeh 1
Ali Kamandi 2
Hossein Rahami 1

1 School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

2 Department of Algorithms and Computation, School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

https://doi.org/10.22059/jac.2023.92758
Abstract
Predicting missing links in noisy protein-protein interaction networks is an essential~computational method. Recently, attributed network embedding methods have been shown to be significantly effective in generating low-dimensional representations of nodes to predict links; in these representations, both the nodes'features and the network's topological information are preserved. Recent research suggests that models based on paths of length 3 between two nodes are more accurate than models based on paths of length 2 for predicting missing links in a protein-protein interaction network. In the present study, an attributed network embedding method termed ANE-SITI is recommended to combine protein sequence information and network topological information. In addition, to improve accuracy, network topological information also considers paths of length 3 between two proteins. The results of this experiment demonstrate that ANE-SITI outperforms the compared methods on various~protein-protein interaction (PPI) networks.
 

Keywords

link prediction
protein-protein interaction networks
attributed network embedding
biased random walks
Journal of Algorithms and Computation
Volume 55, Issue 1
June 2023
Pages 79-99

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us