Title page for etd-0907110-202928


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URN etd-0907110-202928
Author Hsin-Wei Yen
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2009
Semester 2
Degree Master
Type of Document
Language English
Title Improvement of Protein All-atom Prediction with SVM
Date of Defense 2010-07-07
Page Count 68
Keyword
  • prediction
  • tool preference
  • backbone
  • protein
  • Abstract There are many studies have been devoted to solve the all-atom protein back- bone reconstruction problem (PBRP), such as Adcock’s method, MaxSprout, SAB- BAC and Chang’s method. In the previous work, Wang et al. tried to solve this problem by homology modeling. Then, Chang et al. improved Wang’s result by refining the positions of oxygen based on the AMBER force field. We compare the results in CASP7 and 8 from Chang et al. and SABBAC v1.2 and find that some proteins get better predicting results by Chang’s method and others do better in SABBAC. Based on SVM, we propose a tool preference classification method for determining which tool is potentially the better one for predicting the structure of a target protein. We design a series of steps to select the better feature sets for SVM. Our method is tested on the proteins with standard amino acids in CASP7 and 8 dataset, which contains 30 and 24 protein sequences, respectively. The experimen- tal results show that our method has 7.39% and 2.94% RMSD improvement against Chang’s result in CASP7 and 8, respectively. Our method can also be applied to other effective prediction methods, even if they will be developed in the future.
    Advisory Committee
  • Shih-Chung Chen - chair
  • Chung-Lung Cho - co-chair
  • Shyue-Horng Shiau - co-chair
  • Jyh-Jian Sheu - co-chair
  • Chang-Biau Yang - advisor
  • Files
  • etd-0907110-202928.pdf
  • indicate in-campus access in a year and off_campus not accessible
    Date of Submission 2010-09-07

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