An Extensive Assessment of Network Embedding in PPI Network Alignment
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 p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/5/730 |
| _version_ | 1827669054345707520 |
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| author | Marianna Milano Chiara Zucco Marzia Settino Mario Cannataro |
| author_facet | Marianna Milano Chiara Zucco Marzia Settino Mario Cannataro |
| author_sort | Marianna Milano |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:55:09Z |
| format | Article |
| id | doaj.art-607729a112ca4475b952ff9f79900d5d |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T03:55:09Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-607729a112ca4475b952ff9f79900d5d2023-11-23T10:56:20ZengMDPI AGEntropy1099-43002022-05-0124573010.3390/e24050730An Extensive Assessment of Network Embedding in PPI Network AlignmentMarianna Milano0Chiara Zucco1Marzia Settino2Mario Cannataro3Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, ItalyDepartment of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, ItalyDepartment of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, ItalyDepartment of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, ItalyNetwork 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.https://www.mdpi.com/1099-4300/24/5/730network embeddingnetwork alignmentPPI |
| spellingShingle | Marianna Milano Chiara Zucco Marzia Settino Mario Cannataro An Extensive Assessment of Network Embedding in PPI Network Alignment Entropy network embedding network alignment PPI |
| title | An Extensive Assessment of Network Embedding in PPI Network Alignment |
| title_full | An Extensive Assessment of Network Embedding in PPI Network Alignment |
| title_fullStr | An Extensive Assessment of Network Embedding in PPI Network Alignment |
| title_full_unstemmed | An Extensive Assessment of Network Embedding in PPI Network Alignment |
| title_short | An Extensive Assessment of Network Embedding in PPI Network Alignment |
| title_sort | extensive assessment of network embedding in ppi network alignment |
| topic | network embedding network alignment PPI |
| url | https://www.mdpi.com/1099-4300/24/5/730 |
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