Temporal gene expression classification with regularised neural network

This paper proposes regularised neural networks for characterisation of the multiple heterogeneous temporal dynamic patterns of gene expressions. Regularisation is developed to deal with noisy, high dimensional time course data and overfitting problems. We test the proposed model with a popular gene expression data. The model's performance is compared to other classification techniques, such as Nearest Neighbour, Support Vector Machine, and Self Organised Map. Results show that the proposed model can effectively capture the dynamic feature of gene expression temporal patterns despite the high noise levels, the highly correlated attributes, the overwhelming interactions, and other complex features typically present in microarray data.

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