Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/60844
標題: KStable:使用Kstar搭配regular-mRMR特徵選擇法預測蛋白質單點突變後熱穩定性之改變
KStable: Predicting protein stability changes by K-star with regular-mRMR feature selection
作者: 何承偉
Ho, Cheng-Wei
關鍵字: 蛋白質熱穩定性
protein thermostability
機器學習法
mRMR特徵選擇
regular-mRMR
machine learning
mRMR feature selection
regular-mRMR
出版社: 基因體暨生物資訊學研究所
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摘要: 蛋白質熱穩定性的潛在應用領域非常廣泛,如提高蛋白質活性、研究蛋白質互相作用位點的結構特性和藥物開發等。到目前為止,預測工具大多考慮3D結構作為預測蛋白質穩定性的資訊,然而卻有更多蛋白質是只知道一級序列的資訊。本研究提出一個使用序列預測蛋白質熱穩定性之預測系統KStable,在七大類別共58個機器學習法裡選擇表現最好的Kstar搭配由我們首先提出的regular-mRMR特徵選擇法讓來自Protherm資料庫的資料集進行學習。經過十倍交叉驗證後預測準確度為0.83,並與其他預測蛋白質穩定性的網站:AUTO-MUTE、i-Mutant、Mupro、PopMuSiC及CUPSAT進行比較,最終KStable預測準確度皆勝於其他網站。也因此證明了KStable 使用全新的特徵選擇法不僅減少預測時間且能與參考蛋白質結構的預測工具擁有相同或更佳的預測效能。
Protein thermostability is essential for many studies and industries. Up till now, most prediction tools of protein stability changes considered 3D structure information, however, a large number of proteins only have primary structure. Therefore, this study proposed an effective prediction system, KStable, based on sequence, which adopted Kstar algorithms with regular-mRMR feature selection we first proposed. The prediction accuracy of KStable was 0.83 by 10-fold cross validation in Protherm database. On the other hand, we also compared with the present website tools (AUTO-MUTE, i-Mutant, Mupro, PopMuSiC and CUPSAT) and the prediction accuracy and Matthew’s correlation coefficient of KStable were better than others. Therefore, KStable was proved to reduce the prediction time and keep the prediction performance to those with 3D structure information of tools.
URI: http://hdl.handle.net/11455/60844
其他識別: U0005-3007201216025300
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-3007201216025300
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