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Topological feature generation for link prediction in biological networks
Graph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks. Graph embedding methods learn representations of nodes and interactions in a graph with low-dimensional vectors, which facilitates research to predi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178302/ https://www.ncbi.nlm.nih.gov/pubmed/37187525 http://dx.doi.org/10.7717/peerj.15313 |
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author | Temiz, Mustafa Bakir-Gungor, Burcu Güner Şahan, Pınar Coskun, Mustafa |
author_facet | Temiz, Mustafa Bakir-Gungor, Burcu Güner Şahan, Pınar Coskun, Mustafa |
author_sort | Temiz, Mustafa |
collection | PubMed |
description | Graph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks. Graph embedding methods learn representations of nodes and interactions in a graph with low-dimensional vectors, which facilitates research to predict potential interactions in networks. However, most graph embedding methods suffer from high computational costs in the form of high computational complexity of the embedding methods and learning times of the classifier, as well as the high dimensionality of complex biological networks. To address these challenges, in this study, we use the Chopper algorithm as an alternative approach to graph embedding, which accelerates the iterative processes and thus reduces the running time of the iterative algorithms for three different (nervous system, blood, heart) undirected protein-protein interaction (PPI) networks. Due to the high dimensionality of the matrix obtained after the embedding process, the data are transformed into a smaller representation by applying feature regularization techniques. We evaluated the performance of the proposed method by comparing it with state-of-the-art methods. Extensive experiments demonstrate that the proposed approach reduces the learning time of the classifier and performs better in link prediction. We have also shown that the proposed embedding method is faster than state-of-the-art methods on three different PPI datasets. |
format | Online Article Text |
id | pubmed-10178302 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101783022023-05-13 Topological feature generation for link prediction in biological networks Temiz, Mustafa Bakir-Gungor, Burcu Güner Şahan, Pınar Coskun, Mustafa PeerJ Computational Biology Graph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks. Graph embedding methods learn representations of nodes and interactions in a graph with low-dimensional vectors, which facilitates research to predict potential interactions in networks. However, most graph embedding methods suffer from high computational costs in the form of high computational complexity of the embedding methods and learning times of the classifier, as well as the high dimensionality of complex biological networks. To address these challenges, in this study, we use the Chopper algorithm as an alternative approach to graph embedding, which accelerates the iterative processes and thus reduces the running time of the iterative algorithms for three different (nervous system, blood, heart) undirected protein-protein interaction (PPI) networks. Due to the high dimensionality of the matrix obtained after the embedding process, the data are transformed into a smaller representation by applying feature regularization techniques. We evaluated the performance of the proposed method by comparing it with state-of-the-art methods. Extensive experiments demonstrate that the proposed approach reduces the learning time of the classifier and performs better in link prediction. We have also shown that the proposed embedding method is faster than state-of-the-art methods on three different PPI datasets. PeerJ Inc. 2023-05-09 /pmc/articles/PMC10178302/ /pubmed/37187525 http://dx.doi.org/10.7717/peerj.15313 Text en © 2023 Temiz et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Computational Biology Temiz, Mustafa Bakir-Gungor, Burcu Güner Şahan, Pınar Coskun, Mustafa Topological feature generation for link prediction in biological networks |
title | Topological feature generation for link prediction in biological networks |
title_full | Topological feature generation for link prediction in biological networks |
title_fullStr | Topological feature generation for link prediction in biological networks |
title_full_unstemmed | Topological feature generation for link prediction in biological networks |
title_short | Topological feature generation for link prediction in biological networks |
title_sort | topological feature generation for link prediction in biological networks |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178302/ https://www.ncbi.nlm.nih.gov/pubmed/37187525 http://dx.doi.org/10.7717/peerj.15313 |
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