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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...

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Autores principales: Wei, Xiaohan, Zhang, Yulai, Wang, Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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