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JACIII Vol.11 No.7 pp. 751-758
doi: 10.20965/jaciii.2007.p0751
(2007)

Paper:

A Pseudo Data Generation Method and a Two-Stage Quantitation Method for Simultaneous Determination Sensor of Nucleotide Derivatives

Akito Fukuda*, Sayaka Kondo**, Kenichi Maruyama**,
Koji Suzuki**, and Masafumi Hagiwara*

*Department of Information and Computer Science, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan

**Department of Applied Chemistry, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan

Received:
January 11, 2007
Accepted:
May 2, 2007
Published:
September 20, 2007
Keywords:
neural network, chemical sensor, data analysis
Abstract

In this paper, we propose a pseudo data generation method and a two-stage quantitation method for simultaneous determination of nucleotide derivatives sensor that determines concentration of nucleotide derivatives based on sensor response. Conventional sensors are difficult to determine concentration of nucleotide derivatives simultaneously because they have similar structures and they influence each other, so the precision is low. In order to archive high precision and simultaneous determination sensor, this paper proposes a pseudo data generation method and a two-stage quantitation method. To analyze sensor response, we use GRNN (General Regression Neural Network). With this sensor, concentration of nucleotide derivatives is determined simultaneously, easily and fast. It was confirmed by the experiments that proposed methods are effective for determining concentration of nucleotide derivatives.

Cite this article as:
Akito Fukuda, Sayaka Kondo, Kenichi Maruyama,
Koji Suzuki, and Masafumi Hagiwara, “A Pseudo Data Generation Method and a Two-Stage Quantitation Method for Simultaneous Determination Sensor of Nucleotide Derivatives,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.7, pp. 751-758, 2007.
Data files:
References
  1. [1] S. W. Millward, T. Takahashi, and R. W. Roberts, “A General Route for Post-Translational Cyclization of mRNA Display Libraries,” Journal of American Chemical Society, Vol.127, pp. 14142-14143, 2002.
  2. [2] A. Sugiyama and K. G. Lurie, “An Enzymatic Fluorometric Assay for Adenosine 3’:5’-Monophosphate,” Analytical Biochemistry, Vol.218, pp. 20-25, 1994.
  3. [3] K. Seya, K. Furukawa, and S. Motomura, “A Fluorometric Assay for Cyclic Guanosine39, 59-Monophosphate Incorporating a Sep-PakCartridge and Enzymatic Cycling,” Analytical Biochemistry, Vol.272, pp. 243-249, 1999.
  4. [4] F. M. Battiston, J.-P. Ramseyer, H. P. Lang, M. K. Baller, C. Gerber, J. K. Gimzewski, E. Meyer, and H.-J. Guntherodt, “Chemical sensor based on a microfabricated cantilever array with simultaneous resonance-frequency and bending readout,” Sensor and Actuators B, Vol.77, pp. 122-131, 2001.
  5. [5] Y. Cheng, Y. Zhang, and J. A. McCammon, “How Does the cAMPDependent Protein Kinase Catalyze the Phosphorylation Reaction: An ab Initio QM/MM Study,” Journal of American Chemical Society, Vol.127, No.5, pp. 1553-1562, 2005.
  6. [6] M. Frajnt, M. Cytrynska, and T. Jakubowicz, “The effect of cAMP and cGMP on the activity and substratespecificity of protein kinase A from methylotrophic yeastPichia pastoris,” Acta Biochimica Polonica, Vol.50, No.4, pp. 1111-1118, 2003.
  7. [7] B. Lavine and J. Workman Jr., “Chemometrics,” Anal. Chem., Vol.74, No.12, pp. 2763-2770, 2002.
  8. [8] M. Hagiwara, “Neural network, fuzzy system and genetic algorithm,” Sangyotosyo, 1994.
  9. [9] M. Pardo and G. Sberveglieri, “Remarks on the Use of Multilayer Perceptrons forthe Analysis of Chemical Sensor Array Data,” IEEE Sensor Journal, Vol.4, No.3, pp. 355-363, 2004.
  10. [10] D. F. Specht, “A General Regression Neural Network,” IEEE Trans. on Neural Networks, Vol.2, No.6, pp. 568-576, 1991.
  11. [11] G. A. Carpenter, S. Grossberg, and D. B. Rosen, “Fuzzy ART: an adaptive resonance algorithm for rapid, stable classification of analog patterns,” IEEE Trans. on Neural Networks, Vol.2, pp. 411-416, 1991.
  12. [12] R. Leszek, “Generalized Regression Neural Networks in Time-Varying Environment,” IEEE Trans. on Neural Networks, Vol.15, No.3, pp. 576-596, 2004.
  13. [13] D. Gerry, “Evolving Robot Behavior via Interactive Evolutionary Computation: From Real-World to Simulation,” Proceedings of the 2005 ACM symposium on Applied computing, pp. 340-344, 2005.

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