Cascaded multiple classifiers for secondary structure prediction
Cascaded multiple classifiers for secondary structure prediction
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DOI：
10.1110/ps.9.6.1162
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发表时间：
2000-06-01
期刊：
影响因子：
8
通讯作者：
King, RD
中科院分区：
文献类型：
--
作者：
Ouali, M;King, RD
我们描述了一种蛋白质二级结构预测的NEU分类器，该预测是通过使用神经网络和线性歧视将不同类型的分类器一起级联组成的。新的分类器在496个非同源序列（从G.J. Barton和J.A. Cuff获得的新的非冗余数据集上，可实现76.7％（通过严格的全刀刀程序评估）的精度为76.7％。该数据库专门设计用于训练和测试蛋白质二级结构预测方法，并且与以前的研究相比，它对同源序列的定义更为严格。我们表明，可以设计可以高度区分三类（H，E，C）的分类器，仅使用本地窗口和重新采样技术，其准确性高达78％。这表明对预测β链的预测的重要性远程相互作用可能先前被高估了。
We describe a neu classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. The new classifier achieves an accuracy of 76.7% (assessed by a rigorous full Jack-knife procedure) on a new nonredundant dataset of 496 nonhomologous sequences (obtained from G.J. Barton and J.A. Cuff). This database was especially designed to train and test protein secondary structure prediction methods, and it uses a more stringent definition of homologous sequence than in previous studies. We show that it is possible to design classifiers that can highly discriminate the three classes (H, E, C) with an accuracy of up to 78% for beta-strands, using only a local window and resampling techniques. This indicates that the importance ut long-range interactions for the prediction of beta-strands has been probably previously overestimated.