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Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks
Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important i...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600785/ https://www.ncbi.nlm.nih.gov/pubmed/37420371 http://dx.doi.org/10.3390/e24101351 |
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author | Wei, Xiaohan Zhang, Yulai Wang, Cheng |
author_facet | Wei, Xiaohan Zhang, Yulai Wang, Cheng |
author_sort | Wei, Xiaohan |
collection | PubMed |
description | Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly. |
format | Online Article Text |
id | pubmed-9600785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96007852022-10-27 Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks Wei, Xiaohan Zhang, Yulai Wang, Cheng Entropy (Basel) Article Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly. MDPI 2022-09-24 /pmc/articles/PMC9600785/ /pubmed/37420371 http://dx.doi.org/10.3390/e24101351 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wei, Xiaohan Zhang, Yulai Wang, Cheng Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks |
title | Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks |
title_full | Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks |
title_fullStr | Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks |
title_full_unstemmed | Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks |
title_short | Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks |
title_sort | bayesian network structure learning method based on causal direction graph for protein signaling networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600785/ https://www.ncbi.nlm.nih.gov/pubmed/37420371 http://dx.doi.org/10.3390/e24101351 |
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