A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding. (October 2021)

• Record Type: Journal Article
• Authors: Nasiri, Elahe; Berahmand, Kamal; Rostami, Mehrdad; Dabiri, Mohammad
• Is Part Of: Computers in biology and medicine. Volume 137(2021)
• Publication Date: 2021-10
• Subjects: Protein-protein interaction network -- Link prediction -- Graph embedding -- Random walk -- Feature selection; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104772
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The prediction of interactions in protein networks is very critical in various biological processes. In recent years, scientists have focused on computational approaches to predict the interactions of proteins. In protein-protein interaction (PPI) networks, each protein is accompanied by various features, including amino acid sequence, subcellular location, and protein domains. Embedding-based methods have been widely applied for many network analysis tasks, such as link prediction. The Deepwalk algorithm is one of the most popular graph embedding methods that capture the network structure using pure random walking. Here in this paper, we treat the protein-protein interaction prediction problem as a link prediction in attributed networks, and we use an attributed embedding approach to predict the interactions between proteins in the PPI network. In particular, the present paper seeks to present a modified version of Deepwalk based on feature selection for solving link prediction in the protein-protein interaction, which will benefit both network structure and protein features. More specifically the feature selection step consists of two distinct parts. First, a set of relevant features are selected from the original feature set, such that the dimensionality of features is reduced. Second, in the selected set of features, each feature is assigned with a weight based on its significance and therefore the contribution of each feature is distinguished from others. In this method, the new random walk model for link prediction will be introduced by integrating network structure and protein features, based on the assumption that two nodes on the network will be linked since they are nearby in the network. In order to justify the proposal, the authors carry out many experiments on protein-protein interaction networks for comparison with the state-of-the-art network embedding methods. The experimental results from the graphs indicate that our proposed approach is more capable compared to other link prediction approaches and increases the accuracy of prediction. Highlights: We proposed a biased random-walk-based embedding method for the problem of PPI prediction. We used structural and non-structural properties of PPINs to learn more effective representations. We performed a feature selection step to reduce the features number and select only the most relevant features for each protein. We demonstrate that the FSFDW outperforms all state-of-the-art embedding methods on real-world PPINs.