Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Javed Khan, et al.: Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks. Nature Medicine, 7(6), 2001, Pages 673–679.
Helen C. Causton, John Quackenbush and Alvis Brazma: Microarray Gene Expression Data Analysis: a Beginner’s Guide. Blackwell Publishing, 2003.
Michael Hornquist, John Hertz and Mattias Wahde: Effective Dimensionality of Large-Scale Expression Data Using Principal Component Analysis. BioSystem, 65, 2003, Pages 147–156.
E. C. Keedwell and A. Narayanan: Genetic Algorithms for Gene Expression Analysis. First European Workshop on Evolutionary Bioinformatics (2002), Pages 76–86.
Paul T. Spellman, et al.: Comprehensive Identification of Cell Cycle-regulated genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell, 9, December 1998, Pages 3273–3297.
Raymond J. Cho, et al: A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle. Molecular Cell, 2, July 1998, Pages 65–73.
Jose C. Principe, Neil R. Euliano and W. Curt Lefebvre: Neural and Adaptive Systems: Fundamentals through Simulations. John Wiley & Sons, 2000.
Vogl, T. P., J.K. Mangis, A.K. Rigler, W.T. Zink, and D.L. Alkon: Accelerating the convergence of the backpropagation method. Biological Cybernetics, vol. 59, pp. 257–263, 1988.
Walter Enders: Applied Econometric Time Series. Wiley, 1995.
Hoo, K.A., Sinzinger E.D., and Piovoso, M.J.: Improvements in the predictive capability of neural networks. Journal of Process Control, vol. 12, pp. 193–202, 2002.
Ritchie, M.D., White, B.C., Parker, J.S., Hahn, L.W, and Moore, J.H.: Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics, 4:28, 2003.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ao, SI., Ng, M.K., Ching, W. (2005). Modeling Gene Expression Network with PCA-NN on Continuous Inputs and Outputs Basis. In: Zhang, W., Tong, W., Chen, Z., Glowinski, R. (eds) Current Trends in High Performance Computing and Its Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27912-1_20
Download citation
DOI: https://doi.org/10.1007/3-540-27912-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25785-1
Online ISBN: 978-3-540-27912-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)