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Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm
The explosion of genomic data provides new opportunities to improve the task of gene regulatory network reconstruction. Because of its inherent probability character, the Bayesian network is one of the most promising methods. However, excessive computation time and the requirements of a large number...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6071145/ https://www.ncbi.nlm.nih.gov/pubmed/29986472 http://dx.doi.org/10.3390/genes9070342 |
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author | Xing, Linlin Guo, Maozu Liu, Xiaoyan Wang, Chunyu Zhang, Lei |
author_facet | Xing, Linlin Guo, Maozu Liu, Xiaoyan Wang, Chunyu Zhang, Lei |
author_sort | Xing, Linlin |
collection | PubMed |
description | The explosion of genomic data provides new opportunities to improve the task of gene regulatory network reconstruction. Because of its inherent probability character, the Bayesian network is one of the most promising methods. However, excessive computation time and the requirements of a large number of biological samples reduce its effectiveness and application to gene regulatory network reconstruction. In this paper, Flooding-Pruning Hill-Climbing algorithm (FPHC) is proposed as a novel hybrid method based on Bayesian networks for gene regulatory networks reconstruction. On the basis of our previous work, we propose the concept of DPI Level based on data processing inequality (DPI) to better identify neighbors of each gene on the lack of enough biological samples. Then, we use the search-and-score approach to learn the final network structure in the restricted search space. We first analyze and validate the effectiveness of FPHC in theory. Then, extensive comparison experiments are carried out on known Bayesian networks and biological networks from the DREAM (Dialogue on Reverse Engineering Assessment and Methods) challenge. The results show that the FPHC algorithm, under recommended parameters, outperforms, on average, the original hill climbing and Max-Min Hill-Climbing (MMHC) methods with respect to the network structure and running time. In addition, our results show that FPHC is more suitable for gene regulatory network reconstruction with limited data. |
format | Online Article Text |
id | pubmed-6071145 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-60711452018-08-09 Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm Xing, Linlin Guo, Maozu Liu, Xiaoyan Wang, Chunyu Zhang, Lei Genes (Basel) Article The explosion of genomic data provides new opportunities to improve the task of gene regulatory network reconstruction. Because of its inherent probability character, the Bayesian network is one of the most promising methods. However, excessive computation time and the requirements of a large number of biological samples reduce its effectiveness and application to gene regulatory network reconstruction. In this paper, Flooding-Pruning Hill-Climbing algorithm (FPHC) is proposed as a novel hybrid method based on Bayesian networks for gene regulatory networks reconstruction. On the basis of our previous work, we propose the concept of DPI Level based on data processing inequality (DPI) to better identify neighbors of each gene on the lack of enough biological samples. Then, we use the search-and-score approach to learn the final network structure in the restricted search space. We first analyze and validate the effectiveness of FPHC in theory. Then, extensive comparison experiments are carried out on known Bayesian networks and biological networks from the DREAM (Dialogue on Reverse Engineering Assessment and Methods) challenge. The results show that the FPHC algorithm, under recommended parameters, outperforms, on average, the original hill climbing and Max-Min Hill-Climbing (MMHC) methods with respect to the network structure and running time. In addition, our results show that FPHC is more suitable for gene regulatory network reconstruction with limited data. MDPI 2018-07-06 /pmc/articles/PMC6071145/ /pubmed/29986472 http://dx.doi.org/10.3390/genes9070342 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xing, Linlin Guo, Maozu Liu, Xiaoyan Wang, Chunyu Zhang, Lei Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm |
title | Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm |
title_full | Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm |
title_fullStr | Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm |
title_full_unstemmed | Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm |
title_short | Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm |
title_sort | gene regulatory networks reconstruction using the flooding-pruning hill-climbing algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6071145/ https://www.ncbi.nlm.nih.gov/pubmed/29986472 http://dx.doi.org/10.3390/genes9070342 |
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