a nonlinear pattern recognition of pandemic h1n1 using a state space based methods

genomic signal processing is a relatively new field in bioinformatics, in which signal processing algorithms and methods are used to study functional structures in the dna. an appropriate mapping of the dna sequence into one or more numerical sequences enables the use of many digital signal processing tools in the analysis of different genomic sequences. also, a novel influenza a (h1n1) virus of swine origin emerged in the spring of 2009 and spread very rapidly among people. the severity of the disease and the number of deaths caused by a pandemic virus varies greatly and can change over time. throughout this work, pandemic h1n1 genomic sequences were characterized according to nonlinear dynamical features such as moment invariants and largest lyapunov exponents and then compared to those features that extracted from classical h1n1 genomic sequences. the proposed methods were applied to a number of sequences encoded into a time series using a coding measure scheme employing electron-ion interaction pseudopotential (eiip). the aim of this work is to extract genomic features that can distinguish the new swine flu from the classical h1n1 existed before using sequences from segment 8 of the influenza genome that consists of 8 rna segments which encodes two important proteins for immune system attack (ns1 and ns2). according to the obtained results it is evident that variability is present based on a significance test in both groups; pandemic and classical h1n1 sequences.