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URN	etd-0907110-202928
Author	Hsin-Wei Yen
Author's Email Address	No Public.
Statistics	This thesis had been viewed 482 times. Download 0 times.
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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