Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Xing, Linlin, Guo, Maozu, Liu, Xiaoyan, Wang, Chunyu, Zhang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
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
_version_ 1783343817606299648
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
work_keys_str_mv AT xinglinlin generegulatorynetworksreconstructionusingthefloodingpruninghillclimbingalgorithm
AT guomaozu generegulatorynetworksreconstructionusingthefloodingpruninghillclimbingalgorithm
AT liuxiaoyan generegulatorynetworksreconstructionusingthefloodingpruninghillclimbingalgorithm
AT wangchunyu generegulatorynetworksreconstructionusingthefloodingpruninghillclimbingalgorithm
AT zhanglei generegulatorynetworksreconstructionusingthefloodingpruninghillclimbingalgorithm