Time lagged recurrent neural network for temporal gene expression classification

摘要

Heterogeneous gene expressions provide insight into the biological role of gene interaction with the environment, disease development and drug effect at the molecular level. We propose Time Lagged Recurrent Neural Network with trajectory learning for identifying and classifying gene functional patterns from the heterogeneous nonlinear time series microarray experiments. The proposed procedures identify gene functional patterns from the dynamics of a state-trajectory learned in the heterogeneous time series and the gradient information over time. Trajectory learning with Back-propagation through time algorithm can recognise gene expression patterns vary over time. This reveals more information about the regulatory network underlying gene expressions.