Modeling gene expression time-series with radial basis function neural networks

Abstract

Gene expression time-series are discrete, noisy, short and usually unevenly sample& Most existing methods used to "npare.expression -profiles operate directly on the time points. While.modelling the profiles can lead to more generalised, smooth characterisation of gene expressions. In this paper.a Radial Basis Function neural network is employed to model .gene expression time-series. The .Orthogonal Least Square method, used for selection of centres, is further combined with a width optimisation scheme. The experiments on a number of expression dalasets have shown the advantages of the approach in terms of generalisation and approximation. The results on ,known datasets have indeed.coincided with biological interpretations.