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

Gene expression time-series are discrete, noisy, short and usually unevenly sampled. Most of the existing methods used to compare 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 datasets have shown the advantages of the approach in terms of generalisation and approximation. The results on known datasets have indeed coincided with biological interpretations.