Protein Crystallization Prediction with AdaBoost

doi:10.1504/IJDMB.2013.053197

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/93415

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/93415

题名:	Protein Crystallization Prediction with AdaBoost
作者:	Hsieh, Cheng-Wei;Hsu, Hui-Huang;Pai, Tun-Wen
贡献者:	淡江大學資訊工程學系
关键词:	X-ray crystallography;protein crystallization;feature selection;SVM: support vector machines;AdaBoost;data mining;bioinformatics
日期:	2013-03
上传时间:	2014-01-09 15:03:20 (UTC+8)
出版者:	Olney: Inderscience Publishers
摘要:	To determine the structure of a protein by X-ray crystallography, the protein needs to be purified and crystallized first. However, some proteins cannot be crystallized. This makes the average cost of protein structure determination much higher. Thus it is desired to predict the crystallizability of a protein by a computational method before starting the wet-lab procedure. Features from the primary structure of a target protein are collected first. With a proper set of features, protein crystallizability can be predicted with a high accuracy. In this research, 74 features from previous researches are re-examined by two filter-mode feature selection methods. The selected features are then used for crystallization prediction by three versions of AdaBoost. The Support Vector Machines (SVMs) are also tested for comparison. The best prediction accuracy of AdaBoost reaches 93 percent and 48 important features are identified from the collected 74 features.
關聯:	International Journal of Data Mining and Bioinformatics 7(2), pp.214-227
DOI:	10.1504/IJDMB.2013.053197
显示于类别:	[資訊工程學系暨研究所] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	65	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章

数据加载中.....