Classification of gene functions using support vector machine for time-course gene expression data

Since most biological systems are developmental and dynamic, time-course gene expression profiles provide an important characterization of gene functions. Assigning functions for genes with unknown functions based on time-course gene expressions is an important task in functional genomics. Recently, various methods have been proposed for the classification of gene functions based on time-course gene expression data. In this paper, we consider the classification of gene functions from functional data analysis viewpoint, where a functional support vector machine is adopted. The functional support vector machine can model temporal effects of time-course gene expression data by incorporating the coefficients as well as the basis matrix obtained from a finite expansion of gene expressions on a set of basis functions. We apply the functional support vector machine to both real microarray and simulated data. Our results indicate that the functional support vector machine is effective in discriminating gene functions of time-course gene expressions with predefined functions. The method also provides valuable functional information about interactions between genes and allows the assignment of new functions to genes with unknown functions.