Noise Analysis of Time Series Data in Gene Regulatory Networks
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, BSpline 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.
Haixin Wang James E. Glover
Department of Mathematics and Computer Science Fort Valley State University Fort Valley, GA 31030 USA
国际会议
上海
英文
1860-1864
2011-10-15(万方平台首次上网日期,不代表论文的发表时间)