Using gene expression programming to infer gene regulatory networks from time-series data |
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| Affiliation: | 1. College of Computer Science, Sichuan University, Chengdu 610065, PR China;2. College of Mathematic, Sichuan University, Chengdu 610065, PR China;3. College of Chemistry, Sichuan University, Chengdu 610064, PR China;1. School of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, China;2. Department of Physics, National University of Singapore, Singapore 117542, Singapore;3. Risk Management Institute, National University of Singapore, Singapore 117542, Singapore;4. Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia;5. Zagreb School of Economics and Management, 10000 Zagreb, Croatia;1. Lithuanian Energy Institute, Laboratory of Combustion Processes, Breslaujos g. 3, 44403 Kaunas, Lithuania;2. Université du Luxembourg, Campus Kirchberg, 6, rue Coudenhove-Kalergi, L-1359 Luxembourg, Luxembourg |
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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. |
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| Keywords: | Gene regulatory networks Ordinary differential equation Gene expression programming Least mean square |
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