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An ANN model for treatment prediction in HBV patients



Sajid Iqbal1, 2*, Khalid Masood1, Osman Jafer2



1Bioinformatics lab, National Center of Excellence in Molecular Biology, Lahore, Pakistan; 2Central veterinary research laboratory, Department of molecular biology and genetics, Dubai, United Arab Emirates


Email; *Corresponding author


Article Type




Received May 16, 2011; Accepted June 01, 2011; Published June 06, 2011



Two types of antiviral treatments, namely, interferon and nucleoside/nucleotide analogues are available for hepatitis infections. The selection of drug and dose determined using known pharmacokinetics and pharmacodynamics data is important. The lack of sufficient information for pharmacokinetics of a drug may not produce the desired results. Artificial neural network (ANN) provides a novel model-independent approach to pharmacokinetics and pharmacodynamics data. ANN model is created by supervised learning of 90 patients sample to predict the treatment strategy (lamivudine only and Lamivudine + Interferon) on the basis of viral load, liver function test, visit number, treatment duration, ethnic area, sex, and age. The model was trained with 68 (77.3%) samples and tested with 20 (22.7%) samples. The model produced 92% accuracy with 92.8% sensitivity and 83.3% specificity.



ANN (Artificial neural networks), Hepatitis, Prediction, Treatment


Iqbal et al. Bioinformation 6(6): 237-239 (2011)

Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.