An Improved Method for Completely Uncertain Biological Network Alignment
With the continuous development of biological experiment technology, more and more data related to uncertain biological networks needs to be analyzed. However, most of current alignment methods are designed for the deterministic biological network. Only a few can solve the probabilistic network alig...
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| Format: | Online |
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Hindawi Publishing Corporation
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426770/ |
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pubmed-4426770 |
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pubmed-44267702015-05-21 An Improved Method for Completely Uncertain Biological Network Alignment Shen, Bin Zhao, Muwei Zhong, Wei He, Jieyue Research Article With the continuous development of biological experiment technology, more and more data related to uncertain biological networks needs to be analyzed. However, most of current alignment methods are designed for the deterministic biological network. Only a few can solve the probabilistic network alignment problem. However, these approaches only use the part of probabilistic data in the original networks allowing only one of the two networks to be probabilistic. To overcome the weakness of current approaches, an improved method called completely probabilistic biological network comparison alignment (C_PBNA) is proposed in this paper. This new method is designed for complete probabilistic biological network alignment based on probabilistic biological network alignment (PBNA) in order to take full advantage of the uncertain information of biological network. The degree of consistency (agreement) indicates that C_PBNA can find the results neglected by PBNA algorithm. Furthermore, the GO consistency (GOC) and global network alignment score (GNAS) have been selected as evaluation criteria, and all of them proved that C_PBNA can obtain more biologically significant results than those of PBNA algorithm. Hindawi Publishing Corporation 2015 2015-04-27 /pmc/articles/PMC4426770/ /pubmed/26000284 http://dx.doi.org/10.1155/2015/253854 Text en Copyright © 2015 Bin Shen et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Shen, Bin Zhao, Muwei Zhong, Wei He, Jieyue |
| spellingShingle |
Shen, Bin Zhao, Muwei Zhong, Wei He, Jieyue An Improved Method for Completely Uncertain Biological Network Alignment |
| author_facet |
Shen, Bin Zhao, Muwei Zhong, Wei He, Jieyue |
| author_sort |
Shen, Bin |
| title |
An Improved Method for Completely Uncertain Biological Network Alignment |
| title_short |
An Improved Method for Completely Uncertain Biological Network Alignment |
| title_full |
An Improved Method for Completely Uncertain Biological Network Alignment |
| title_fullStr |
An Improved Method for Completely Uncertain Biological Network Alignment |
| title_full_unstemmed |
An Improved Method for Completely Uncertain Biological Network Alignment |
| title_sort |
improved method for completely uncertain biological network alignment |
| description |
With the continuous development of biological experiment technology, more and more data related to uncertain biological networks needs to be analyzed. However, most of current alignment methods are designed for the deterministic biological network. Only a few can solve the probabilistic network alignment problem. However, these approaches only use the part of probabilistic data in the original networks allowing only one of the two networks to be probabilistic. To overcome the weakness of current approaches, an improved method called completely probabilistic biological network comparison alignment (C_PBNA) is proposed in this paper. This new method is designed for complete probabilistic biological network alignment based on probabilistic biological network alignment (PBNA) in order to take full advantage of the uncertain information of biological network. The degree of consistency (agreement) indicates that C_PBNA can find the results neglected by PBNA algorithm. Furthermore, the GO consistency (GOC) and global network alignment score (GNAS) have been selected as evaluation criteria, and all of them proved that C_PBNA can obtain more biologically significant results than those of PBNA algorithm. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426770/ |
| _version_ |
1613221659758559232 |