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Volume 13, Issue 4 (1-2011)                   J Arak Uni Med Sci 2011, 13(4): 95-105 | Back to browse issues page

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Kenarkoohi A, soleimanjahi H, Falahi S, Riahi Madvar H, Meshkat Z. The application of the new intelligent Adaptive Nero Fuzzy Inference System (ANFIS) in prediction of human papilloma virus oncogenicity potency. J Arak Uni Med Sci. 2011; 13 (4) :95-105
URL: http://jams.arakmu.ac.ir/article-1-566-en.html
1- , soleim_h@modares.ac.ir
Abstract:   (16655 Views)
Background: Based on the severity and prognostic condition of respective cancers caused by them, papilloma viruses are classified into high, medium, and low risk groups using E6 and E7 viral proteins. Nowadays, different methods of modeling in clinical medicine are used for diagnosis of diseases and evaluation of their molecular characteristics. Among the new methods of modeling, fuzzy systems are of particular importance in various fields of science. The aim of this study was to use a new intelligent Adaptive Nero Fuzzy Inference System (ANFIS) for predicting human papilloma virus oncogenicity based on a number of biochemical properties of E7 protein. Materials and Methods: In this study, using ANFIS model, a new model was developed for predicting oncogenicity of papilloma virus isolated from patients. The process of training and testing was performed using a set of available published filed data and several statistical and graphical criteria. Accordingly, through provision of needed biochemical and biophysical data on E7 gens from the existing data, this model was developed. The results of this model were, then, validated by the authentic published data. Results: Based on the results, the developed model is capable of predicting papilloma virus oncogenicity efficiently. R2 and RMSE values in training stage were 0.99 and 101.18, respectively. In the testing stage, however, they stood at 0.94 and 173.8, respectively. Conclusion: Based on the findings, the use of ANFIS model significantly improves the accuracy of estimating virus oncogenicity phenomenon. The methodology presented in this study is a new approach in estimating viral oncogenicity and can successfully be combined with other mathematical models for model updating in real conditions.
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Subject: Infection
Received: 2009/12/21

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