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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Exploiting Temporal Genetic Correlations for Enhancing Regulatory Network Optimization
verfasst von : Ahammed Sherief Kizhakkethil Youseph, Madhu Chetty, Gour Karmakar
Erschienen in: Neural Information Processing
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Abstract
Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference.