Using LogitBoost classifier to predict protein structural classes
Using LogitBoost classifier to predict protein structural classes
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DOI：
10.1016/j.jtbi.2005.05.034
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发表时间：
2006-01-07
影响因子：
2
通讯作者：
Chou, KC
中科院分区：
文献类型：
--
作者：
Cai, YD;Feng, KY;Chou, KC
蛋白质分类预测是分子生物学中的一个重要课题。这是因为它不仅能够从结构本身的角度提供有用的信息，而且还极大地刺激了可能与其生物学功能密切相关的蛋白质的许多其他特征的表征。本文介绍了最近发展起来的Boosting算法之一LogitBoost，并将其用于蛋白质结构类的预测。它使用回归方案作为基本学习者来执行分类，该方案可以处理多类问题，并且在处理噪声数据方面特别上级。结果表明，LogitBoost在预测给定数据集的结构类方面优于支持向量机，表明新分类器非常有前途。如果LogitBoost算法和其他一些现有的算法能够有效地相互补充，预计预测蛋白质结构类以及许多其他生物大分子属性的能力将进一步加强。(c)2005爱思唯尔有限公司保留所有权利。
Prediction of protein classification is an important topic in molecular biology. This is because it is able to not only provide useful information from the viewpoint of structure itself, but also greatly stimulate the characterization of many other features of proteins that may be closely correlated with their biological functions. In this paper, the LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes. It performs classification using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data. It was demonstrated that the LogitBoost outperformed the support vector machines in predicting the structural classes for a given dataset, indicating that the new classifier is very promising. It is anticipated that the power in predicting protein structural classes as well as many other biomacromolecular attributes will be further strengthened if the LogitBoost and some other existing algorithms can be effectively complemented with each other. (c) 2005 Elsevier Ltd. All rights reserved.