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A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

Xiao X, Zhang W, Zou X - PLoS ONE (2015)

Bottom Line: However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost.In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization.Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan, China.

ABSTRACT
The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

No MeSH data available.


Related in: MedlinePlus

Difference of two methods LSGPA and PCA-CMI in four indexes.
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pone.0119294.g004: Difference of two methods LSGPA and PCA-CMI in four indexes.

Mentions: Figs. 3 and 4 depicted the difference of the LSGPA with other two methods NARROMI and PCA-CMI based on the four indexes, i.e., using the indicator value for the proposed LSGPA minus the value for other methods. We can clearly see that the vast majority of the comparison values are over the zero line, which means that the performance of our algorithm is much better than the other methods, especially in larger sets.


A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

Xiao X, Zhang W, Zou X - PLoS ONE (2015)

Difference of two methods LSGPA and PCA-CMI in four indexes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4373852&req=5

pone.0119294.g004: Difference of two methods LSGPA and PCA-CMI in four indexes.
Mentions: Figs. 3 and 4 depicted the difference of the LSGPA with other two methods NARROMI and PCA-CMI based on the four indexes, i.e., using the indicator value for the proposed LSGPA minus the value for other methods. We can clearly see that the vast majority of the comparison values are over the zero line, which means that the performance of our algorithm is much better than the other methods, especially in larger sets.

Bottom Line: However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost.In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization.Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan, China.

ABSTRACT
The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

No MeSH data available.


Related in: MedlinePlus