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Variable predictive model based classification algorithm for effective separation of protein structural classes

机译：基于变量预测模型的有效分离蛋白质结构类别的分类算法

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摘要

Variable predictive model based class discrimination (VPMCD) algorithm is proposed as an effective protein secondary structure classification tool. The algorithm mathematically represents the characteristics amino acid interactions specific to each protein structure and exploits them further to distinguish different structures. The new concept and the VPMCD classifier are established using well-studied datasets containing four protein classes as benchmark. The protein samples selected from SCOP and PDB databases with varying homology (25-100%) and non-uniform distribution of class samples provide challenging classification problem. The performance of the new method is compared with advanced classification algorithms like component coupled, SVM and neural networks. VPMCD provides superior performance for high homology datasets. 100% classification is achieved for self-consistency test and an improvement of 5% prediction accuracy is obtained during Jackknife test. The sensitivity of the new algorithm is investigated by varying model structures/types and sequence homology. Simpler to implement VPMCD algorithm is observed to be a robust classification technique and shows potential for effective extensions to other clinical diagnosis and data mining applications in biological systems.
机译：提出了一种基于可变预测模型的分类识别（VPMCD）算法，作为一种有效的蛋白质二级结构分类工具。该算法在数学上表示每种蛋白质结构特有的氨基酸相互作用的特征，并进一步利用它们来区分不同的结构。新概念和VPMCD分类器是使用经过精心研究的数据集建立的，该数据集包含四个蛋白质类别作为基准。从SCOP和PDB数据库中选择的具有不同同源性（25-100％）和类别样本分布不均匀的蛋白质样本提供了具有挑战性的分类问题。将该新方法的性能与诸如组件耦合，SVM和神经网络之类的高级分类算法进行了比较。 VPMCD为高同源性数据集提供了卓越的性能。自一致性测试可实现100％的分类，而Jackknife测试期间可提高5％的预测准确性。通过改变模型结构/类型和序列同源性来研究新算法的敏感性。观察到更易于实现的VPMCD算法是一种强大的分类技术，它显示了有效扩展到生物系统中其他临床诊断和数据挖掘应用程序的潜力。

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