Time lagged recurrent neural network for temporal gene expression classification

Authors:
Yulan Liang;Arpad Kelemen
Affiliations:
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
Venue:
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Year:
2009

Citing 3
Cited 1

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
A dynamical system perspective of structural learning with forgetting

IEEE Transactions on Neural Networks
Gradient calculations for dynamic recurrent neural networks: a survey

IEEE Transactions on Neural Networks

Automated classification of galaxies using invariant moments

FGIT'12 Proceedings of the 4th international conference on Future Generation Information Technology

Quantified Score

Hi-index	0.00

Visualization

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.