首页> 外文会议>Computational Intelligence in Bioinformatics and Computational Biology, 2009. CIBCB '09 >Protein fold recognition with adaptive local hyperplane algorithm
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Protein fold recognition with adaptive local hyperplane algorithm

机译:自适应局部超平面算法的蛋白质折叠识别

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Protein fold recognition task is important for understanding the biological functions of proteins. The adaptive local hyperplane (ALH) algorithm has been shown to perform better than many other renown classifiers including support vector machines, K-nearest neighbor, linear discriminant analysis, K-local hyperplane distance nearest neighbor algorithms and decision trees on a variety of data sets. In this paper, we apply the ALH algorithm to well-known data sets on protein fold recognition task without sequence similarity from Ding and Dubchak (2001). The results obtained demonstrate that the ALH algorithm outperforms all the seven other very well known and established benchmarking classifiers applied to same data sets.
机译:蛋白质折叠识别任务对于理解蛋白质的生物学功能很重要。自适应局部超平面(ALH)算法的性能优于许多其他知名分类器,包括支持向量机,K近邻,线性判别分析,K局部超平面距离最近邻算法和各种数据集上的决策树。在本文中,我们将ALH算法应用于蛋白质折叠识别任务的知名数据集,而Ding和Dubchak(2001)没有序列相似性。获得的结果表明,ALH算法优于应用于相同数据集的所有其他七个非常著名的基准基准分类器。

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