Modelling and Clustering of Gene Expressions Using RBFs and a Shape Similarity Metric

This paper introduces a novel approach for gene expression time-series modelling and clustering using neural networks and a shape similarity metric. The modelling of gene expressions by the Radial Basis Function (RBF) neural networks is proposed to produce a more general and smooth characterisation of the series. Furthermore, we identified that the use of the correlation coefficient of the derivative of the modelled profiles allows the comparison of profiles based on their shapes and the distributions of time points. The series are grouped into similarly shaped profiles using a correlation based fuzzy clustering algorithm. A well known dataset is used to demonstrate the proposed approach and a set of known genes are used as a benchmark to evaluate its performance. The results show the biological relevance and indicate that the proposed method is a useful technique for gene expression time-series analysis.