Combining classifiers for improved classification of proteins from sequence or structure.
Combining classifiers for improved classification of proteins from sequence or structure.
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DOI：
10.1186/1471-2105-9-389
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发表时间：
2008-09-22
影响因子：
3
通讯作者：
Noble WS
中科院分区：
文献类型：
--
作者：
Melvin I;Weston J;Leslie CS;Noble WS
从氨基酸序列或结构预测蛋白质的结构或功能类别是计算生物学中的一个基本问题。最近，人们对使用判别学习算法（特别是支持向量机（SVM））进行蛋白质分类产生了很大的兴趣。然而，由于需要足够多的正例来训练此类分类器，因此所有基于 SVM 的方法都受到覆盖范围有限的阻碍。在这项研究中，我们开发了一种用于蛋白质分类的混合机器学习方法，并将该方法应用于根据蛋白质的序列或 3D 结构将蛋白质分配到结构类别的问题。该方法将全覆盖但精度较低的最近邻方法与精度较高但覆盖范围减小的多类支持向量机相结合，以产生整体精度提高的全覆盖分类器。混合方法基于使用学习阈值从一种方法“投注”到另一种方法的简单想法。在 SCOP 层次结构的交叉验证实验中，混合方法在所有覆盖级别上始终优于单个组件方法。代码和数据集可在
Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage. In this study, we develop a hybrid machine learning approach for classifying proteins, and we apply the method to the problem of assigning proteins to structural categories based on their sequences or their 3D structures. The method combines a full-coverage but lower accuracy nearest neighbor method with higher accuracy but reduced coverage multiclass SVMs to produce a full coverage classifier with overall improved accuracy. The hybrid approach is based on the simple idea of "punting" from one method to another using a learned threshold. In cross-validated experiments on the SCOP hierarchy, the hybrid methods consistently outperform the individual component methods at all levels of coverage. Code and data sets are available at
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DOI：
10.1142/s021972000500120x
发表时间：
2005-06-01
影响因子：
1
作者：
Kuang, Rui;Ie, Eugene;Leslie, Christina
通讯作者：
Leslie, Christina
影响因子：
7.8
作者：
WOLPERT, DH
通讯作者：
WOLPERT, DH
影响因子：
5.8
作者：
Shamim, Mohammad Tabrez Anwar;Anwaruddin, Mohammad;Nagarajaram, H. A.
通讯作者：
Nagarajaram, H. A.
DOI：
10.1093/protein/11.9.739
发表时间：
1998-09-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
Shindyalov, IN;Bourne, PE
通讯作者：
Bourne, PE
影响因子：
5.8
作者：
Qiu, Jian;Hue, Martial;Noble, William Stafford
通讯作者：
Noble, William Stafford