Detection Of Proteins Information Based On Wavelet And Fuzzy Relationship

Post-genome research ultimately aims to understand a complete set of proteins in a living process for their expression pattern and biological function.Protein-protein interaction plays a central role in the movement of life. To analyze the topology structure characters of protein-protein interaction networks brings further understandings to the structure and function of cells at the proteome level, which may set a new direction to predict the functions of unknown proteins and eventually lead to the discovery of new drug targets and design methods. With the protein-protein interaction data continuously enriched, protein-protein interaction network construction has made great development. The thesis constructs a fuzzy relationship of protein-protein interaction, which turns the relationship of protein- protein interaction network topology characteristics from a High-dimensional relationships into a One-dimensional one. Through the pretreatment of the fuzzy relationship we defined, we can establish a character that is irrelevant to the node of the protein network. For processing statistical analysis of related coefficients, the thesis proposes an algorithm to serialize them, and then processes it with wavelet transform.This thesis detected the singular points of the high-frequency and low-frequency signal of six species, by computing Lipschitz exponent. Finally, this thesis presents the conclusion that the number of the singular points of the high-frequency signal increases with the degree of biological evolution.