Modeling Of Gene Network Information Of Influenza Viruses And Its Application In Coarse Grain Space

In this paper, based on the theory of granular computing, the structural clustering of fuzzy proximity relation is constructed. Combined with the physical-chemical properties of amino acids, the structure of H1N1 influenza virus is established and the characteristics of H1N1 influenza virus are analyzed. The research results of H1N1 influenza virus are enriched. The specific works are as follows:In chapter one, the bioinformatics and biological network, granular computing theory and optimization clustering index, sequence and spatial structure of the influenza A (H1N1)virus, the main work and innovations of this paper are introduced briefly.In chapter two, based on the traditional 40 dimensional feature vector of protein sequences, according to amino acids classification and the physical-chemical properties, the 40 dimensional vector is decomposed into 20,4 and 16 dimensional feature vector. In the condition of the characterization of virus sequence features is not affected of the premises, the existing characterization of protein sequences of 40-dimensional eigenvector can be replaced by 16-dimensional eigenvector by the method of system clustering and correlation analysis.In chapter three, based on the chapter two and the properties of HA and NA protein, the new feature vector of the H1N1 flu virus protein sequences is introduced. The optimal cluster of the H1N1 influenza virus occurred from 1902 to 2013 is obtained by the new feature vector and the structural clustering.The relationship between HA and NA protein structures and the outbreak of H1N1 virus protein sequences is analyzed by selecting the representative elements and constructing evolutionary tree.In chapter four, based on the theories of fuzzy granular space, the properties of the the inter-class and intra-class deviations are studied and proved. The new optimal index and corresponding algorithm are introduced. The two layers of the H1N1 influenza virus occurred from 1902 to 2013 is constructed by the new feature vector introduced in chapter three and the new optimizing clustering algorithm. The validation of the new method and the influenza virus’s hierarchical structure model is showed by analyzing the character of the H1N1 influenza virus.In chapter five, the whole paper is summarized, and the possible research directions and ideas are analyzed.