Abstract
In this paper, the quantitative analysis of time-series gene expression data on inference of gene regulatory networks is performed. Time-series gene data are modeled by the B-Spline algorithm to improve the overall smooth expression curves which can further reduce over-fitting. The effect of the different sizes of observed time-series data on gene regulatory networks inference is analyzed. The stochastic errors introduced by the B-Spline algorithm to the system are evaluated. The precision of different sizes of time-series data on parameter estimations is compared. With application of the B-Spline to generate continuous curves, simulation results can be much more accurate and inference results are significantly improved. Both synthetic data and experimental data from microarray measurements are used to demonstrate the effectiveness of the proposed method.
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