Application of Self-Organizing map for Time-varying biological to predicting of complexity Systems

Application of Self-Organizing map for Time-varying biological to predicting of complexity Systems
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© 2020 by IJCTT Journal
Volume-68 Issue-4
Year of Publication : 2020
Authors : Mohammad Almasi, Mahla Alimardani
DOI :  10.14445/22312803/IJCTT-V68I4P111

How to Cite?

Mohammad Almasi, Mahla Alimardani, "Application of Self-Organizing map for Time-varying biological to predicting of complexity Systems," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 59-64, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I4P111

Abstract
One of the problems in biochemistry is how to estimate the biological system`s behavior changes during the time. The types of recursive identification methods like recursive least square error (RLSE) estimation and Kalman filter is used in previous, the speed of estimation is still a limiting factor in biological systems. It is especially noticeable when the identifier has to estimate the parameters in the situation that it had estimated before but has later lost the estimated values because of the changes in the biological systems behavior. To overcome this problem and speed up the identification process, the multiple model identification using the self-organizing map neural network (MMSOM) has been introduced. In multiple modeling, there is more than one estimated model for biological systems. In each step of time, the best model is selected for the biological systems according to previously defined criteria of identifications which is adaptive during different times.

Keywords
Biological Prediction . deep learning . Neural Network . SOM

Reference
[1] J. MacKerell, A. D., D. Bashford, M. Bellot, J. Dunbrack, R. L., J. D. Evanseck, M. J. Field, S. Fischer, J.Gao, H. Guo, S. Ha, D. Joseph-McCarthy, L. Kuchnir, K. Kuczera, F. T. K. Lau, C. Mattos, S. Michnick, T. Ngo, D. T. Nguyen, B. Prodhom, I. Reiher, W. E., B. Roux, M. Schlenkrich, J. C. Smith, R. Stote, J. Straub, M. Watanabe, J. Wiorkiewicz-Kuczera, D. Yin, and M. Karplus, “All-atom empirical potential for Molecular modeling and dynamics studies of proteins,” Journal of Physical Chemistry B, vol. 102, no. 18, pp. 3586–3616, (1998).
[2] D. A. Case, J. T. Berryman, R. M. Betz, D. S. Cerutti, T. E. C. III, T. A. Darden, R. E. Duke, T. J. Giese,H. Gohlke, A. W. Goetz, N. Homeyer, S. Izadi, P. Janowski, J. Kaus, A. Kovalenko, T. S. Lee, S. LeGrand,P. Li, T. Luchko, R. Luo, B. Madej, K. M. Merz, G. Monard, P. Needham, H. Nguyen, H. T. Nguyen, I. Omelyan, A. Onufriev, D. R. Roe, A. Roitberg, R. Salomon-Ferrer, C. L. Simmerling, W. Smith, J. Swails, R. C. Walker, J. Wang, R. Wolf, X. Wu, D. M. York, and P. A. Kollman, “Amber 2015,” University of California, San Francisco, (2015).
[3] N. A. Baker, “Poisson-Boltzmann methods for biomolecular electrostatics,” Methods in Enzymology, vol. 383, pp. 94–118, (2004).
[4] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, (2015).
[5] B. Roux and T. Simonson, “Implicit solvent models,” Biophysical Chemistry, vol. 78, no. 1-2, pp. 1–20, (1999).
[6] J. A. McCammon, B. R. Gelin, and M. Karplus, “Dynamics of folded proteins,” Nature, vol. 267, pp. 585–590, (1977).
[7] J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal deep learning,” in Proceedings of the 28th international conference on machine learning (ICML-11), pp. 689–696, (2011).
[8] M. Almasi, H. Fathi, S. Adel, and S. Samiee, “Human Action Recognition through the First-Person Point of view, Case Study Two Basic Task,” International Journal of Computer Applications, vol. 177, no. 24, pp. 19–23, 2019.
[9] M. Almasi, “An Investigation on Face Detection Applications,” International Journal of Computer Applications, vol. 177, no. 21, pp. 17–23, 2019.
[10] Pawan Suresh Upadhye, Parag Suresh Upadhye "Classification Rule Extraction using Artificial Neural Network"International Journal of Computer Trends and Technology (IJCTT),V4(3):441-445 Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
[11] Yuslena Sari, Ricardus Anggi Pramunendar "Classification Quality of Tobacco Leaves as Cigarette Raw Material Based on Artificial Neural Networks". International Journal of Computer Trends and Technology (IJCTT) V50(3):147-150, August 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
[12] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” ArXiv preprint arXiv: 1409.1556, (2014).
[13] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The Shared views of four research groups,” IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82–97, (2012).
[14] Y. Zhang, H. Yu, J. H. Qin, and B. C. Lin, “A microfluidic dna computing processor for gene expression Analysis and gene drug synthesisn,” Biomicrofluidics, vol. 3, no. 044105, (2009).
[15] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural Networks,” in Advances in neural information processing systems, pp. 1097–1105, (2012).
[16] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85–117, 2015.
[17] A. Warshel and A. Papazyan, “Electrostatic effects in macromolecules: fundamental concepts and practical modeling,” Current Opinion in Structural Biology, vol. 8, no. 2, pp. 211–7, (1998).
[18] F. Dong, B. Olsen, and N. A. Baker, “Computational methods for biomolecular electrostatics,” Methods in Cell Biology, vol. 84, pp. 843–70. (2008).
[19] K. A. Sharp and B. Honig, “Electrostatic interactions in macromolecules - theory and applications,” Annual Review of Biophysics and Biophysical Chemistry, vol. 19, pp. 301–332, (1990).
[20] Q. Cui, “Combining implicit solvation models with hybrid quantum mechanical/molecular mechanical methods: A critical test with glycine,” Journal of Chemical Physics, vol. 117, no. 10, p. 4720, (2002).
[21] M. J. Holst, “Multilevel Methods for the Poisson-Boltzmann Equation. Urbana-Champaign: University of Illinois at Urbana-Champaign”, Numerical Computing Group, (1993).
[22] D. M. Tully-Smith and H. Reiss, “Further development of scaled particle theory of rigid sphere fluids,” Journal of Chemical Physics, vol. 53, no. 10, pp. 4015–25, (1970).
[23] A. Warshel and M. Levitt, “Theoretical studies of enzymic reactions: Dielectric, electrostatic and steric Stabilization of the carbonium ion in the reaction of lysozyme,” Journal of Molecular Biology, vol. 103,pp. 227–249, (1976).