Using gene expression programming to infer gene regulatory networks from time-series data

Authors:
Yongqing Zhang;Yifei Pu;Haisen Zhang;Yabo Su;Lifang Zhang;Jiliu Zhou
Affiliations:
-;-;-;-;-;-
Venue:
Computational Biology and Chemistry
Year:
2013

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Abstract

Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively.