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Noise analysis of time series data in gene regulatory networks

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2 Author(s)
Haixin Wang ; Dept. of Math. & Comput. Sci., Fort Valley State Univ., Fort Valley, GA, USA ; Glover, J.E.

One of the most important properties in gene expression is the stochasticity. Gene expression process is noisy and fluctuant. In this paper, the quantitative analysis of noisy time-series gene expression data on inference of gene regulatory networks is performed. We propose a two-step algorithm to solve the problem. In the first step, B-Spline is introduced to interpolate between data points. In the second step, Kalman filter or H filter is introduced to infer the gene structure. If the statistical noise is known, Kalman filter is applied; Otherwise H filter is applied. Both synthetic data and real experiment data are used to evaluate the procedure.

Published in:

Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on  (Volume:4 )

Date of Conference:

15-17 Oct. 2011