Modeling and Optimization of Oligonucleotide-Based Nanobiosensor Using Artificial Neural Network and Genetic Algorithm Based Procedure

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

IMANI AYDIN; HOSSEINPOUR SOLEIMAN; Azimzadeh Mostafa; KEYHANI ALIREZA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Iranian Journal of Biosystems Engineering Year:2020 | Volume:51 | Issue:1 Page(s): 171-181

Download Full-Text

Persian Verion

View:

603

Download:

Cites:

Information Journal Paper

Title

Modeling and Optimization of Oligonucleotide-Based Nanobiosensor Using Artificial Neural Network and Genetic Algorithm Based Procedure

Author(s)

IMANI AYDIN | HOSSEINPOUR SOLEIMAN | Azimzadeh Mostafa | KEYHANI ALIREZA | Issue Writer Certificate

Keywords

NanobiosensorQ1

ModelingQ2

Artificial Neural NetworkQ2

OptimizationQ2

Genetic AlgorithmQ1

Abstract

Developing a biosensor faces the different challenges for parameter Optimization and calibration. In this study, a machine learning based approach is used to model and optimize the effective parameters of an electrochemical Nanobiosensor based on thiolated probe-functionalized gold nanorods (GNRs) decorated on the graphene oxide (GO) sheet on the surface of a glassy carbon electrode (GCE). The response of the biosensor was considered as the output and eight effective factors including GO concentration, GNR concentration, probe concentration, probe time, MCH time, hybridization time, Oracet Blue (OB) concentration, and OB incubation time were used as inputs to train and model an Artificial Neural Network. The experimental results demonstrate that the output of the developed model has an acceptable compatibility with the results obtained in the laboratory. The developed model is able to predict the output of the Nanobiosensor with accuracy of 96. 91% and the mean absolute percentage error (MAPE) value of 5. 5090 %. Finally, Genetic Algorithm is used to find the optimum values of these parameters which yield the maximum value of the Nanobiosensor output. The Optimization results indicated that this method has better performance compared to the laboratory results and this method can be used for Nanobiosensor design.

Cites

No record.

References

No record.

Cite

APA: Copy

IMANI, AYDIN, HOSSEINPOUR, SOLEIMAN, & KEYHANI, ALIREZA. (2020). Modeling and Optimization of Oligonucleotide-Based Nanobiosensor Using Artificial Neural Network and Genetic Algorithm Based Procedure. IRANIAN JOURNAL OF BIOSYSTEMS ENGINEERING (IRANIAN JOURNAL OF AGRICULTURAL SCIENCES), 51(1 ), 171-181. SID. https://sid.ir/paper/144522/en

Vancouver: Copy

IMANI AYDIN, HOSSEINPOUR SOLEIMAN, KEYHANI ALIREZA. Modeling and Optimization of Oligonucleotide-Based Nanobiosensor Using Artificial Neural Network and Genetic Algorithm Based Procedure. IRANIAN JOURNAL OF BIOSYSTEMS ENGINEERING (IRANIAN JOURNAL OF AGRICULTURAL SCIENCES)[Internet]. 2020;51(1 ):171-181. Available from: https://sid.ir/paper/144522/en

IEEE: Copy

AYDIN IMANI, SOLEIMAN HOSSEINPOUR, and ALIREZA KEYHANI, “Modeling and Optimization of Oligonucleotide-Based Nanobiosensor Using Artificial Neural Network and Genetic Algorithm Based Procedure,” IRANIAN JOURNAL OF BIOSYSTEMS ENGINEERING (IRANIAN JOURNAL OF AGRICULTURAL SCIENCES), vol. 51, no. 1 , pp. 171–181, 2020, [Online]. Available: https://sid.ir/paper/144522/en

Related Journal Papers