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The Study on Protein Classification Based on Protein Hasse Matrix Image

Author: XuPeiJie
Tutor: XiaoXuan
School: Jingdezhen Ceramic Institute
Course: Control Theory and Control Engineering
Keywords: Bioinformatics Teature-Extraction Hasse Matrix Partial Ordering
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 31
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Abstract


Classificasion of protein sequence and visualization technology are importante part of protein research. The premise and basis of how to command the structure and function of protein study is how to classify a lot of protein sequences, and the visualization technology will make the study more easy.In this paper, we studied the characteristic extreaction and visualization technology carefully, and present a new method to classify protein sequence which is based on protein Hasse matrix image, and we did many experiments on standard dataset, the success rate get much better than other methods.According to the partial ordering technique, we construct an improved Hasse matrix for each protein sequence and visualize the matrix by colorific graphic for intuitionistic analysis. Moreover, for quantitative analysis, we would transform the improved Hasse matrix into Gray Level Co-occurrence Matrix (GLCM) and put up a comparison degree of similarity among different protein sequences. We did our study on predicting G-protein-coupled, Predicting Secretory Proteins and Secondary structure. The mainwork in this thesis are shown as followed:(1) Amino acid numeric coding was used to add physico-chemical property into numeric sequence. The coded model reflected the physical chemistry characters of amino acid, like Hydrophobicity, Hydrophilicity and Side-chain mass. So, three numeric sequences will be got from one protein sequence.(2) Partial ordering and Hasse matrix will be used into bioinformatics. The hasse matrix is constructed based on parlial ordering: We can convert the protein sequence to three numeric serials based on the coded model, then construct three Hasse matrixes based on partial ordering. Elements in the three matrixes only have two numbers:“0”and“1”. Then we integrate the three basic Hasse matrixes into an improved Hasse matrix which contains the three physical chemistry characters.(3) The improved Hasse matrix has 8 elements:“0”、“1”、“2”、“3”、“4”、“5”、“6”、and“7”.At last, we transform the improved Hasse matrix to a image which has 8 colors.“0”is black,“1”is blue,“2”is green and so on.(4) Using protein Hasse matrix image and Image pattern recognition to predicting G-protein-coupled, Predicting Secretory Proteins and Secondary structure. The results are all very good.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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