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The Characteristic Analysis of the Protein Secondery Structure and Protein Interaction Prediction

Author: JuHong
Tutor: WangYaDong
School: Harbin Institute of Technology
Course: Computer Science and Technology
Keywords: Machine Learning Maximum Entropy Model Support Vector Machine Protein Secondary Structure Prediction Protein-protein interaction prediction
CLC: Q51
Type: Master's thesis
Year: 2009
Downloads: 71
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Abstract


With the completion of the Human Genome Project , biology towards the post-genomic era marked functional genomics . Plays an important role as a research branch of the post-genomic era , proteomics research , this is due to the organisms perform life activities without the participation of the protein and the interactions between them . With the growing and mature protein sequencing technology , X - ray diffraction techniques and protein function analysis method , it can get a lot of protein sequence , structure and function of the data , this gives us an opportunity to : data-driven approach to prediction of the structure and function of the unknown protein . In this paper, the method of machine learning depth on some important issues in proteomics research . This study includes the following sections: the first , the first analysis of the characteristic properties of the protein sequence used in this paper predict , including physical and chemical characteristics of amino acids and amino acid composition and location characteristics , pattern mining algorithm for each of the species build a human-like language dictionary - Dictionary of protein patterns for each mode of entry is given secondary structure information . And using the mode dictionary entries the least word ideology, the application of the word grid technology to treat predict protein sequence segmentation . Second, in a secondary structure prediction method based on the protein pattern dictionary , and based on the physical and chemical characteristics of the amino acids to build the template of the physical and chemical characteristics , to compensate for unknown words in the dictionary predict protein secondary structure prediction , combined with a maximum entropy model for the secondary structure of proteins to determine the best sequence . This combination of physical and chemical characteristics of amino acids based on the protein secondary structure prediction mode dictionary , Q3 and SOV evaluation , and achieved good results. Third , in the prediction of protein interactions , using a CTD encoding to solve protein sequence length inconsistency led to a different dimension of the input vector . Combined with support vector machine approach to predict protein interactions . This method requires only the protein sequence , not related to the prior knowledge of the protein with the characteristics of general application . And achieved better test results .

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CLC: > Biological Sciences > Biochemistry > Protein
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