SAMNA: accurate alignment of multiple biological networks based on simulated annealing
Proteins are important parts of the biological structures and encode a lot of biological information. Protein–protein interaction network alignment is a model for analyzing proteins that helps discover conserved functions between organisms and predict unknown functions. In particular, multi-network...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-12-01
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| Series: | Journal of Integrative Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jib-2023-0006 |
| _version_ | 1797354336493240320 |
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| author | Chen Jing Wang Zixiang Huang Jia |
| author_facet | Chen Jing Wang Zixiang Huang Jia |
| author_sort | Chen Jing |
| collection | DOAJ |
| description | Proteins are important parts of the biological structures and encode a lot of biological information. Protein–protein interaction network alignment is a model for analyzing proteins that helps discover conserved functions between organisms and predict unknown functions. In particular, multi-network alignment aims at finding the mapping relationship among multiple network nodes, so as to transfer the knowledge across species. However, with the increasing complexity of PPI networks, how to perform network alignment more accurately and efficiently is a new challenge. This paper proposes a new global network alignment algorithm called Simulated Annealing Multiple Network Alignment (SAMNA), using both network topology and sequence homology information. To generate the alignment, SAMNA first generates cross-network candidate clusters by a clustering algorithm on a k-partite similarity graph constructed with sequence similarity information, and then selects candidate cluster nodes as alignment results and optimizes them using an improved simulated annealing algorithm. Finally, the SAMNA algorithm was experimented on synthetic and real-world network datasets, and the results showed that SAMNA outperformed the state-of-the-art algorithm in biological performance. |
| first_indexed | 2024-03-08T13:48:58Z |
| format | Article |
| id | doaj.art-11aa6bcc828e444bb6d6039c7d05818f |
| institution | Directory Open Access Journal |
| issn | 1613-4516 |
| language | English |
| last_indexed | 2024-03-08T13:48:58Z |
| publishDate | 2023-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Integrative Bioinformatics |
| spelling | doaj.art-11aa6bcc828e444bb6d6039c7d05818f2024-01-16T07:18:28ZengDe GruyterJournal of Integrative Bioinformatics1613-45162023-12-012041557110.1515/jib-2023-0006SAMNA: accurate alignment of multiple biological networks based on simulated annealingChen Jing0Wang Zixiang1Huang Jia2School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, ChinaProteins are important parts of the biological structures and encode a lot of biological information. Protein–protein interaction network alignment is a model for analyzing proteins that helps discover conserved functions between organisms and predict unknown functions. In particular, multi-network alignment aims at finding the mapping relationship among multiple network nodes, so as to transfer the knowledge across species. However, with the increasing complexity of PPI networks, how to perform network alignment more accurately and efficiently is a new challenge. This paper proposes a new global network alignment algorithm called Simulated Annealing Multiple Network Alignment (SAMNA), using both network topology and sequence homology information. To generate the alignment, SAMNA first generates cross-network candidate clusters by a clustering algorithm on a k-partite similarity graph constructed with sequence similarity information, and then selects candidate cluster nodes as alignment results and optimizes them using an improved simulated annealing algorithm. Finally, the SAMNA algorithm was experimented on synthetic and real-world network datasets, and the results showed that SAMNA outperformed the state-of-the-art algorithm in biological performance.https://doi.org/10.1515/jib-2023-0006multiple network alignmentprotein–protein interaction networksimulated annealing algorithmnetwork clusteringsequence similarity |
| spellingShingle | Chen Jing Wang Zixiang Huang Jia SAMNA: accurate alignment of multiple biological networks based on simulated annealing Journal of Integrative Bioinformatics multiple network alignment protein–protein interaction network simulated annealing algorithm network clustering sequence similarity |
| title | SAMNA: accurate alignment of multiple biological networks based on simulated annealing |
| title_full | SAMNA: accurate alignment of multiple biological networks based on simulated annealing |
| title_fullStr | SAMNA: accurate alignment of multiple biological networks based on simulated annealing |
| title_full_unstemmed | SAMNA: accurate alignment of multiple biological networks based on simulated annealing |
| title_short | SAMNA: accurate alignment of multiple biological networks based on simulated annealing |
| title_sort | samna accurate alignment of multiple biological networks based on simulated annealing |
| topic | multiple network alignment protein–protein interaction network simulated annealing algorithm network clustering sequence similarity |
| url | https://doi.org/10.1515/jib-2023-0006 |
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