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Article

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; https://doi.org/10.3390/e24050730
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. https://doi.org/10.3390/e24050730

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. https://doi.org/10.3390/e24050730

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. https://doi.org/10.3390/e24050730

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