Hybrid machine learning model for continuous microarray time series

Abstract

A hybrid machine learning model of the principal component analysis and neural network is described for the continuous microarray gene expression time series. The methodology can model numerically the continuous gene expression time series. The proposed model can give us the extracted features from the gene expressions time series with higher prediction accuracies. It can help practitioners to gain a better understanding of a cell cycle, and to find the dependency of genes, which is useful for drug discoveries. In this chapter, we describe the background, the machine learning algorithms, and then the application of the hybrid machine learning in the microarray analysis. The machine learning model is compared with other popular continuous prediction methods. Based on the results of two public microarray datasets, the hybrid method outperforms the other continuous prediction methods. © 2010 Springer Science+Business Media B.V.