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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40207

Title: Protein crystallization prediction with AdaBoost.
Authors: Hsieh CW;Hsu HH;Pai TW.
Contributors: 國立臺灣海洋大學:資訊工程學系
Date: 2013
Issue Date: 2017-01-16T05:46:48Z
Publisher: Int J Data Min Bioinform.
Abstract: Abstract: 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.
Relation: 7(2)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40207
Appears in Collections:[資訊工程學系] 期刊論文

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