Temporal gene expression profiles reconstruction by support vector regression and framelet kernel

Gene time series microarray experiments have been widely used to unravel the genetic machinery of biological process. However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which will make the traditional analyzing methods to be unapplicable. One main approach to solve this problem is to reconstruct each gene expression profile as a continuous function of time. Then the continuous representation enables us to overcome problems related to sampling rate differences and missing values. In this paper, we introduce a novel reconstruction approach based on the support vector regression method. The proposed approach utilizes a framelet based kernel, which has the ability to approximate functions with multiscale structure and can reduce the influence of noise in data. To compensate the inadequate information from noisy and short gene expression data, we use its correlated genes as the test set to choose the optimal parameters. We show that this treatment can help to avoid over-fitting. Experimental results demonstrate that our method can improve the reconstruction accuracy. © 2010 Springer-Verlag.