Particle Swarm Optimization Technique for DNA Sensor Model Based Nanostructured Graphene

Abstract:

Article Preview

DNA biosensors has received significant attention. In particular, we can mention the model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. In this paper, we consider a method of selection of PSO parameters for optimization of the analytical model of a graphene-based DNA sensor. In particular, we consider genetic algorithms, multilayer perceptron networks with gradient learning algorithm, recurrent neural networks with gradient learning algorithm, and 4-order Runge Kutta neural networks with different learning algorithms. Also, we present experimental results for different intelligent algorithms.

Info:

Periodical:

Edited by:

Zhang Mei

Pages:

415-418

Citation:

V. Popov, "Particle Swarm Optimization Technique for DNA Sensor Model Based Nanostructured Graphene", Advanced Materials Research, Vol. 936, pp. 415-418, 2014

Online since:

June 2014

Authors:

Export:

Price:

$38.00

* - Corresponding Author

[1] K. Ishii, C. Tang, Biological DNA Sensor, Academic Press, Chennai, (2013).

[2] J.L. Su, B. -S. Youn, W.P. Ji, J.H. Niazi, S.K. Yeon, B.G. Man, ssDNA aptamer-based surface plasmon resonance biosensor for the detection of retinol binding protein 4 for the early diagnosis of type 2 diabetes, Anal. Chem. 80 (2008) 2867-2873.

DOI: https://doi.org/10.1021/ac800050a

[3] A.L. Liu, G.X. Zhong, J.Y. Chen, S.H. Weng, H.N. Huang, W. Chen, L.Q. Lin, Y. Lei, F.H. Fu, Z.L. Sun, X.H. Lin, J.H. Lin, S.Y. Yang, A sandwich-type DNA biosensor based on electrochemical co-reduction synthesis of graphene-three dimensional nanostructure gold nanocomposite films, Anal. Chim. Acta 767 (2013).

DOI: https://doi.org/10.1016/j.aca.2012.12.049

[4] X. Dong, Y. Shi, W. Huang, P. Chen, L. -J. Li, Electrical detection of DNA hybridization with single-base specificity using transistors based on CVD-grown graphene sheets, Adv. Mater. 22 (2010) 1649-1653.

DOI: https://doi.org/10.1002/adma.200903645

[5] Y. Cui, Q. Wei, H. Park, C.M. Lieber, Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species, Science 293 (2001) 1289-1292.

DOI: https://doi.org/10.1126/science.1062711

[6] P.R. Nair, M.A. Alam, Performance limits of nanobiosensors, Appl. Phys. Lett. 88 (2006) 233120-233123.

DOI: https://doi.org/10.1063/1.2211310

[7] N. Mohanty, V. Berry, Graphene-based single-bacterium resolution biodevice and DNA transistor: interfacing graphene derivatives with nanoscale and microscale biocomponents, Nano Lett. 8 (2008) 4469-4476.

DOI: https://doi.org/10.1021/nl802412n

[8] X. Dong, X. Zhao, L. Wang, W. Huang, Synthesis and application of graphene nanoribbons, Curr. Phys. Chem. 3 (2013) 291-301.

[9] F. Chen, Q. Qing, J. Xia, N. Tao, Graphene field-effect transistors: electrochemical gating, interfacial capacitance, and biosensing applications, Chem. 5 (2010) 2144-2153.

DOI: https://doi.org/10.1002/asia.201000252

[10] M. Zheng, A. Jagota, E.D. Semke, B.A. Diner, R.S. Mclean, S.R. Lustig, R.E. Richardson, N.G. Tassi, DNA-assisted dispersion and separation of carbon nanotubes, Nature Mater. 2 (2003) 338-342.

DOI: https://doi.org/10.1038/nmat877

[11] H. Karimi, R. Yusof, R. Rahmani, M. Ahmadi, Optimization of DNA Sensor Model Based Nanostructured Graphene Using Particle Swarm Optimization Technique, J. Nanomater. 2013 (2013) Article ID 789454.

DOI: https://doi.org/10.1155/2013/789454

[12] A. Jordehi, J. Jasni, Parameter selection in particle swarm optimisation: a survey, J. Exp. Theor. Artif. In. 25 (2013) 527-542.

DOI: https://doi.org/10.1080/0952813x.2013.782348

[13] H.K.F. Abadi, R. Yusof, S.M. Eshrati, S.D. Naghib, M. Rahmani, M. Ghadiri, E. Akbari, M.T. Ahmadi, Current—voltage modeling of graphene-based DNA sensor, Neural Comput. Appl. 24 (2014) 85-89.

DOI: https://doi.org/10.1007/s00521-013-1464-1

[14] Y. -J. Wang, C. -T. Lin, Runge-Kutta neural network for identification of dynamical systems in high accuracy, IEEE T. Neural Networ. 9 (1998) 294-307.

DOI: https://doi.org/10.1109/72.661124