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Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data
BACKGROUND: Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5615243/ https://www.ncbi.nlm.nih.gov/pubmed/28950903 http://dx.doi.org/10.1186/s12918-017-0454-9 |
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author | Fu, Changhe Deng, Su Jin, Guangxu Wang, Xinxin Yu, Zu-Guo |
author_facet | Fu, Changhe Deng, Su Jin, Guangxu Wang, Xinxin Yu, Zu-Guo |
author_sort | Fu, Changhe |
collection | PubMed |
description | BACKGROUND: Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. RESULTS: We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. CONCLUSION: We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously described models. |
format | Online Article Text |
id | pubmed-5615243 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56152432017-09-28 Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data Fu, Changhe Deng, Su Jin, Guangxu Wang, Xinxin Yu, Zu-Guo BMC Syst Biol Research BACKGROUND: Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. RESULTS: We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. CONCLUSION: We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously described models. BioMed Central 2017-09-21 /pmc/articles/PMC5615243/ /pubmed/28950903 http://dx.doi.org/10.1186/s12918-017-0454-9 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Fu, Changhe Deng, Su Jin, Guangxu Wang, Xinxin Yu, Zu-Guo Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data |
title | Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data |
title_full | Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data |
title_fullStr | Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data |
title_full_unstemmed | Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data |
title_short | Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data |
title_sort | bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5615243/ https://www.ncbi.nlm.nih.gov/pubmed/28950903 http://dx.doi.org/10.1186/s12918-017-0454-9 |
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