document: Mining heterogeneous gene expression data with time lagged recurrent neural networks Heterogeneous types of gene expressions may provide a better insight into the biological role of gene interaction with the environment , disease development and drug effect at the molecular level . In this paper for both exploring and prediction purposes a Time Lagged Recurrent Neural Network with trajectory learning is proposed for identifying and classifying the 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 . Also , the trajectory learning with Back-propagation through time algorithm can recognize gene expression patterns vary over time . This may reveal much more information about the regulatory network underlying gene expressions . The analyzed data were extracted from spotted DNA microarrays in the budding yeast expression measurements , produced by Eisen et al. . The gene matrix contained 79 experiments over a variety of heterogeneous experiment conditions . The number of recognized gene patterns in our study ranged from two to ten and were divided into three cases . Optimal network architectures with different memory structures were selected based on Akaike and Bayesian information statistical criteria using two-way factorial design . The optimal model performance was compared to other popular gene classification algorithms such as Nearest Neighbor , Support Vector Machine , and Self-Organized Map . The reliability of the performance was verified with multiple iterated runs .
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