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Improvement of Prediction of Mutation Positions in H5N1 Hemagglutinins of Influenza A Virus Using Neural Network with Distinguishing of Arginine, Leucine and Serine

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In this study, we attempt to improve the prediction of mutation position in H5N1 hemagglutinin from influenza A virus using the neural network model with distinguishing of arginine, leucine and serine. In comparison with previous logistic regression prediction model, the 3-6-1 feedforward backpropagation neural network model reduced independents from seven to three without compromising the predictability, and the prediction is made according to the most similar parameterised hemagglutinin rather than the population means.

Keywords: amino acid; hemagglutinin; influenza; mutation; neural network; prediction; rna; virus

Document Type: Research Article


Publication date: 2007-05-01

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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