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An Extensive Assessment of Network Embedding in PPI Network Alignment

Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Alessandro Giuliani
Entropy 2022, 24(5), 730;
Received: 29 April 2022 / Revised: 18 May 2022 / Accepted: 19 May 2022 / Published: 20 May 2022
Network alignment is a fundamental task in network analysis. In the biological field, where the protein–protein interaction (PPI) is represented as a graph, network alignment allowed the discovery of underlying biological knowledge such as conserved evolutionary pathways and functionally conserved proteins throughout different species. A recent trend in network science concerns network embedding, i.e., the modelling of nodes in a network as a low-dimensional feature vector. In this survey, we present an overview of current PPI network embedding alignment methods, a comparison among them, and a comparison to classical PPI network alignment algorithms. The results of this comparison highlight that: (i) only five network embeddings for network alignment algorithms have been applied in the biological context, whereas the literature presents several classical network alignment algorithms; (ii) there is a need for developing an evaluation framework that may enable a unified comparison between different algorithms; (iii) the majority of the proposed algorithms perform network embedding through matrix factorization-based techniques; (iv) three out of five algorithms leverage external biological resources, while the remaining two are designed for domain agnostic network alignment and tested on PPI networks; (v) two algorithms out of three are stated to perform multi-network alignment, while the remaining perform pairwise network alignment. View Full-Text
Keywords: network embedding; network alignment; PPI network embedding; network alignment; PPI
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MDPI and ACS Style

Milano, M.; Zucco, C.; Settino, M.; Cannataro, M. An Extensive Assessment of Network Embedding in PPI Network Alignment. Entropy 2022, 24, 730.

AMA Style

Milano M, Zucco C, Settino M, Cannataro M. An Extensive Assessment of Network Embedding in PPI Network Alignment. Entropy. 2022; 24(5):730.

Chicago/Turabian Style

Milano, Marianna, Chiara Zucco, Marzia Settino, and Mario Cannataro. 2022. "An Extensive Assessment of Network Embedding in PPI Network Alignment" Entropy 24, no. 5: 730.

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