Abstract
Inference of gene regulatory network (GRN) from gene expression data is still a challenging work. The supervised approaches perform better than unsupervised approaches. In this paper, we develop a new supervised approach based on radial basis function (RBF) neural network for inference of gene regulatory network. A new hybrid evolutionary method based on dissipative particle swarm optimization (DPSO) and firefly algorithm (FA) is proposed to optimize the parameters of RBF. The data from E.coli network is used to test our method and results reveal that our method performs than classical approaches.
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Acknowledgements
This work was supported by the PhD research startup foundation of Zaozhuang University (No. 2014BS13), and Shandong Provincial Natural Science Foundation, China (No. ZR2015PF007).
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Liu, S., Yang, B., Wang, H. (2016). Inference of Gene Regulatory Network Based on Radial Basis Function Neural Network. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_39
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