scholarly journals A Boolean network inference from time-series gene expression data using a genetic algorithm

2018 ◽  
Vol 34 (17) ◽  
pp. i927-i933 ◽  
Author(s):  
Shohag Barman ◽  
Yung-Keun Kwon
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Time Series ◽  
Gene Expression Data ◽  
Boolean Network ◽  
Network Inference ◽  
Expression Data ◽  
Time Series Gene Expression

scholarly journals A neuro-evolution approach to infer a Boolean network from time-series gene expressions

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i762-i769
Author(s):  
Shohag Barman ◽  
Yung-Keun Kwon
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Genetic Algorithm ◽  
Time Series ◽  
Regulatory Network ◽  
Boolean Network ◽  
Network Inference ◽  
Regulatory Function ◽  
Expression Data ◽  
Time Series Gene Expression

Abstract Summary In systems biology, it is challenging to accurately infer a regulatory network from time-series gene expression data, and a variety of methods have been proposed. Most of them were computationally inefficient in inferring very large networks, though, because of the increasing number of candidate regulatory genes. Although a recent approach called GABNI (genetic algorithm-based Boolean network inference) was presented to resolve this problem using a genetic algorithm, there is room for performance improvement because it employed a limited representation model of regulatory functions. In this regard, we devised a novel genetic algorithm combined with a neural network for the Boolean network inference, where a neural network is used to represent the regulatory function instead of an incomplete Boolean truth table used in the GABNI. In addition, our new method extended the range of the time-step lag parameter value between the regulatory and the target genes for more flexible representation of the regulatory function. Extensive simulations with the gene expression datasets of the artificial and real networks were conducted to compare our method with five well-known existing methods including GABNI. Our proposed method significantly outperformed them in terms of both structural and dynamics accuracy. Conclusion Our method can be a promising tool to infer a large-scale Boolean regulatory network from time-series gene expression data. Availability and implementation The source code is freely available at https://github.com/kwon-uou/NNBNI. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Clustering short time series gene expression data

2005 ◽  
Vol 21 (Suppl 1) ◽  
pp. i159-i168 ◽  
Author(s):  
J. Ernst ◽  
G. J. Nau ◽  
Z. Bar-Joseph
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Expression Data ◽  
Short Time Series ◽  
Time Series Gene Expression ◽  
Short Time

Modelling gene interaction networks from time-series gene expression data using evolving spiking neural networks

2019 ◽  
Vol 11 (4) ◽  
pp. 599-613 ◽  
Author(s):  
Elisa Capecci ◽  
Jesus L. Lobo ◽  
Ibai Laña ◽  
Josafath I. Espinosa-Ramos ◽  
Nikola Kasabov
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Time Series ◽  
Gene Expression Data ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Spiking Neural Networks ◽  
Expression Data ◽  
Gene Interaction Networks ◽  
Time Series Gene Expression
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