You can view the full text of this article for free using the link below.

Title: Time lagged recurrent neural network for temporal gene expression classification

Authors: Yulan Liang, Arpad Kelemen

Addresses: Department of Family and Community Health, University of Maryland, 655 W. Lombard. St., Rm 404K, Baltimore, MD, 21201, USA. ' Department of Organisational Systems and Adult Health, University of Maryland, 655 W. Lombard. St., Rm 475A, Baltimore, MD, 21201, USA

Abstract: 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.

Keywords: gene expressions; heterogeneous; time lagged neural networks; trajectory learning; backpropagation through time; gene interaction; gene functional patterns; nonlinear time series microarrays; regulatory network; computational intelligence; bioinformatics.

DOI: 10.1504/IJCIBSB.2009.024053

International Journal of Computational Intelligence in Bioinformatics and Systems Biology, 2009 Vol.1 No.1, pp.86 - 99

Available online: 24 Mar 2009 *

Full-text access for editors Access for subscribers Free access Comment on this article