PREDICTION OF HEPATITIS B VIRUS LAMIVUDINE RESISTANCE BASED ON YMDD SEQUENCE DATA USING AN ARTIFICIAL NEURAL NETWORK MODEL

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RAVANSHAD MEHRDAD; SABAHI FARZANEH; FALAHI SHAHAB; KENAR KOOHI AZRA; AMINI BAVIL OULIAEI S.; HOSSEINI SEYED YOUNES; RIAHI MADVAR HOSSEIN; KHANIZADE SAYAD

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Journal: Hepatitis Monthly Year:2011 | Volume:11 | Issue:2 (31) Page(s): 108-113

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Title

PREDICTION OF HEPATITIS B VIRUS LAMIVUDINE RESISTANCE BASED ON YMDD SEQUENCE DATA USING AN ARTIFICIAL NEURAL NETWORK MODEL

Author(s)

Keywords

DRUG RESISTANCE

LAMIVUDINE

NEURAL NETWORK MODELS

Abstract

Background: Hepatitis B virus (HBV) infection is an important health problem worldwide with critical outcomes. The nucleoside analog LAMIVUDINE (LMV) is a potent inhibitor of HBV polymerase and impedes HBV replication in patients with chronic hepatitis B. Treatment with LMV for long periods causes the appearance and reproduction of drug-resistant strains, rising to more than 40% after 2 years and to over 50% and 70% after 3 and 4 years, respectively.Objectives: Artificial neural networks (ANNs) were used to make predictions with regard to resistance phenotypes using biochemical and biophysical features of the YMDD sequence.Patients and Methods: The study population comprised patients who were intended for surgery in various hospitals in Tehran-Iran. An ACRS-PCR method was performed to distinguish mutations in the YMDD motif of HBV polymerase. In the training and testing stages, these parameters were used to identify the most promising optimal network. The ideal values of RMSE and MAE are zero, and a value near zero indicates better performance. The selection was performed using statistical accuracy measures, such as root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The main purpose of this paper was to develop a new method based on ANNs to simulate HBV DRUG RESISTANCE using the physiochemical properties of the YMDD motif and compare its results with multiple regression models.Results: The results of the MLP in the training stage were 0.8834, 0.07, and 0.09 and 0.8465, 0.160.04 in the testing stage; for the total data, the values were 0.8549, 0.115, and 0.065, respectively. The MLP model predicts LAMIVUDINE resistance in HBV better than the MLR model. Conclusions: The ANN model can be used as an alternative method of predicting the outcome of HBV therapy. In a case study, the proposed model showed vigorous clusterization of predicted and observed drug responses. The current study was designed to develop an algorithm for predicting DRUG RESISTANCE using chemiophysical data with artificially created neural networks. To this end, an intelligent and multidisciplinary program should be developed on the basis of the information to be gained on the essentials of different applications by similar investigations. This program will help design expert neural network architectures for each application automatically.

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Interaction of viral oncogenic proteins with the Wnt signaling pathway

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APA: Copy

RAVANSHAD, MEHRDAD, SABAHI, FARZANEH, FALAHI, SHAHAB, KENAR KOOHI, AZRA, AMINI BAVIL OULIAEI, S., HOSSEINI, SEYED YOUNES, RIAHI MADVAR, HOSSEIN, & KHANIZADE, SAYAD. (2011). PREDICTION OF HEPATITIS B VIRUS LAMIVUDINE RESISTANCE BASED ON YMDD SEQUENCE DATA USING AN ARTIFICIAL NEURAL NETWORK MODEL. HEPATITIS MONTHLY, 11(2 (31)), 108-113. SID. https://sid.ir/paper/306348/en

Vancouver: Copy

RAVANSHAD MEHRDAD, SABAHI FARZANEH, FALAHI SHAHAB, KENAR KOOHI AZRA, AMINI BAVIL OULIAEI S., HOSSEINI SEYED YOUNES, RIAHI MADVAR HOSSEIN, KHANIZADE SAYAD. PREDICTION OF HEPATITIS B VIRUS LAMIVUDINE RESISTANCE BASED ON YMDD SEQUENCE DATA USING AN ARTIFICIAL NEURAL NETWORK MODEL. HEPATITIS MONTHLY[Internet]. 2011;11(2 (31)):108-113. Available from: https://sid.ir/paper/306348/en

IEEE: Copy

MEHRDAD RAVANSHAD, FARZANEH SABAHI, SHAHAB FALAHI, AZRA KENAR KOOHI, S. AMINI BAVIL OULIAEI, SEYED YOUNES HOSSEINI, HOSSEIN RIAHI MADVAR, and SAYAD KHANIZADE, “PREDICTION OF HEPATITIS B VIRUS LAMIVUDINE RESISTANCE BASED ON YMDD SEQUENCE DATA USING AN ARTIFICIAL NEURAL NETWORK MODEL,” HEPATITIS MONTHLY, vol. 11, no. 2 (31), pp. 108–113, 2011, [Online]. Available: https://sid.ir/paper/306348/en

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