Title page for etd-0906111-150445


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URN etd-0906111-150445
Author Dong-Jian Lin
Author's Email Address m983040046@student.nsysu.edu.tw
Statistics This thesis had been viewed 5560 times. Download 1067 times.
Department Computer Science and Engineering
Year 2011
Semester 1
Degree Master
Type of Document
Language English
Title Protein Contact Prediction Based on Protein Sequences
Date of Defense 2011-08-31
Page Count 57
Keyword
  • prediction
  • Contact
  • SVM
  • KNN
  • PDA
  • Abstract The biological function of a protein is mainly maintained by its three-dimensional structure. Protein folds support the three-dimensional structure of a protein, and then the inter-residue contacts in the protein impact the formation of protein folds and the stability of its protein structure. Therefore, the protein contact plays a critical role in building protein structures and analyzing biological functions. In this thesis, we propose a methodology to predict the residue-residue contacts of a target protein and develop a new measurement to evaluate the accuracy of prediction. With three prediction tools, the support vector machine (SVM), the k-nearest neighbor algorithm (KNN), and the penalized discriminant analysis (PDA), we compare these classifiers based on the self-testing of the training set, which are derived from representative protein chains from PDB (PDB-REPRDB), and apply the best (SVM) to predict a testing set of 173 protein chains derived from previous study. The experimental results show that the accuracy of our prediction achieves 24.84%,15.68%, and 8.23% for three categories of different contacts, which greatly improves the result of random exploration (5.31%, 3.33%, and 1.12%, respectively).
    Advisory Committee
  • Chung-Nan Lee - chair
  • Yow-Ling Shiue - co-chair
  • Kuo-Si Huang - co-chair
  • Chang-Biau Yang - advisor
  • Files
  • etd-0906111-150445.pdf
  • Indicate in-campus at 0 year and off-campus access at 1 year.
    Date of Submission 2011-09-06

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