Improving Protein Disorder Prediction by Secondary Structure Information
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 94 === There are increasing quantities of proteins discovered to contain regions that do not form stable tertiary structures in their native states. Such sequence fragments that have no propensity to form specific structures are regarded as “disordered regions”. Some d...
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ndltd-TW-094NTU053921062015-12-16T04:38:37Z http://ndltd.ncl.edu.tw/handle/40456759127091505010 Improving Protein Disorder Prediction by Secondary Structure Information 利用二級結構資訊提昇蛋白質非穩定區段的預測準確度 Tong-Ming Xu 許通明 碩士 國立臺灣大學 資訊工程學研究所 94 There are increasing quantities of proteins discovered to contain regions that do not form stable tertiary structures in their native states. Such sequence fragments that have no propensity to form specific structures are regarded as “disordered regions”. Some disordered regions have been justified to be functionally significant. Therefore, a reliable predictor for such disordered regions is important for further understanding of protein functions. Most recent studies employ the amino acid composition and/or a number of biochemical properties within a sliding window with respect to the target residue as the feature set in predicting protein disorder. In this regard, this thesis conducts a comprehensive study on the performance of a recently proposed feature set which considers both physicochemical properties and amino acid propensity for order/disorder, and demonstrates how a two-stage framework improves the accuracy of the classifier. Furthermore, we propose a novel feature based on protein secondary structures to reduce potential false postives. This thesis attempts several ways of extracting information from the local secondary structures. The experimental results reveal that the feature set taking the distance to the nearest secondary structure element (SSE) of the target residue outperforms the others. In particular, it is observed that employing the proposed feature set in the second stage delivers better accuracies than.that is used together with the original feature sets. Yen-Jen Oyang 歐陽彥正 2006 學位論文 ; thesis 45 zh-TW |
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碩士 === 國立臺灣大學 === 資訊工程學研究所 === 94 === There are increasing quantities of proteins discovered to contain regions that do not form stable tertiary structures in their native states. Such sequence fragments that have no propensity to form specific structures are regarded as “disordered regions”. Some disordered regions have been justified to be functionally significant. Therefore, a reliable predictor for such disordered regions is important for further understanding of protein functions. Most recent studies employ the amino acid composition and/or a number of biochemical properties within a sliding window with respect to the target residue as the feature set in predicting protein disorder. In this regard, this thesis conducts a comprehensive study on the performance of a recently proposed feature set which considers both physicochemical properties and amino acid propensity for order/disorder, and demonstrates how a two-stage framework improves the accuracy of the classifier. Furthermore, we propose a novel feature based on protein secondary structures to reduce potential false postives. This thesis attempts several ways of extracting information from the local secondary structures. The experimental results reveal that the feature set taking the distance to the nearest secondary structure element (SSE) of the target residue outperforms the others. In particular, it is observed that employing the proposed feature set in the second stage delivers better accuracies than.that is used together with the original feature sets.
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Yen-Jen Oyang |
author_facet |
Yen-Jen Oyang Tong-Ming Xu 許通明 |
author |
Tong-Ming Xu 許通明 |
spellingShingle |
Tong-Ming Xu 許通明 Improving Protein Disorder Prediction by Secondary Structure Information |
author_sort |
Tong-Ming Xu |
title |
Improving Protein Disorder Prediction by Secondary Structure Information |
title_short |
Improving Protein Disorder Prediction by Secondary Structure Information |
title_full |
Improving Protein Disorder Prediction by Secondary Structure Information |
title_fullStr |
Improving Protein Disorder Prediction by Secondary Structure Information |
title_full_unstemmed |
Improving Protein Disorder Prediction by Secondary Structure Information |
title_sort |
improving protein disorder prediction by secondary structure information |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/40456759127091505010 |
work_keys_str_mv |
AT tongmingxu improvingproteindisorderpredictionbysecondarystructureinformation AT xǔtōngmíng improvingproteindisorderpredictionbysecondarystructureinformation AT tongmingxu lìyòngèrjíjiégòuzīxùntíshēngdànbáizhìfēiwěndìngqūduàndeyùcèzhǔnquèdù AT xǔtōngmíng lìyòngèrjíjiégòuzīxùntíshēngdànbáizhìfēiwěndìngqūduàndeyùcèzhǔnquèdù |
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1718150420575027200 |