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ISSN: 2155-6210

Journal of Biosensors & Bioelectronics
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Research Article

Application of Artificial Neural Network for Modeling and Prediction of MTT Assay on Human Lung Epithelial Cancer Cell Lines

Taghipour M1,2, Vand AA2, Rezaei A3and Karim GR4*

1Department of Biomedical Engineering, Faculty of medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran

2Department of Computer Engineering, Islamic Azad University, Kermanshah, Iran

3Eelectrical Faculty, Kermanshah University of Technology, Kermanshah, Iran

4Department of Electrical Engineering, Razi University, Kermanshah, Iran

*Corresponding Author:
Karim GR
Department of Electrical Engineering
Razi University, Kermanshah, Iran
Tel: +98 0918237 9045
Fax: +98 831 427 4623
E-mail: [email protected]

Received Date: March 18, 2015 Accepted Date: June 17, 2015 Published Date: June 27, 2015

Citation: Taghipour M, Vand AA, Rezaei A, Karim GR (2015) Application of Artificial Neural Network for Modeling and Prediction of MTT Assay on Human Lung Epithelial Cancer Cell Lines. J Biosens Bioelectron 6:170. doi: 10.4172/2155-6210.1000170

Copyright: © 2015 Taghipour M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

In this paper, a three-layer artificial neural network (ANN) was investigated to predict the inhibitory concentration (IC) values assessed via MTT cell viability assay on the four types of human lung epithelial cancer cell lines. In order to achieve this purpose, a multilayer perceptron (MLP) neural network trained with back-propagation algorithm was employed for developing the ANN model. To develop the model, the input parameters were concentrations and types of cell lines and the outputs were IC10, IC20, IC30, IC40, IC50, IC60, IC70 and IC80 values in the A549, H157, H460 and H1975 cell lines. The proposed ANN model has achieved good agreement with the experimental data and has a small error between the estimated and experimental values. The obtained results show that the proposed ANN model is a useful, reliable, fast and cheap tool to predict the IC values assessed via MTT cell viability assays.

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