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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >FUZZYART NEURAL NETWORK FOR PROTEIN CLASSIFICATION
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FUZZYART NEURAL NETWORK FOR PROTEIN CLASSIFICATION

机译：FUZZYART神经网络用于蛋白质分类

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摘要

One of the major research directions in bioinformatics is that of predicting the protein superfamily in large databases and classifying a given set of protein domains into superfamilies. The classification reflects the structural, evolutionary and functional relatedness. These relationships are embodied in hierarchical classification such as Structural Classification of Protein (SCOP), which is manually curated. Such classification is essential for the structural and functional analysis of proteins. Yet, a large number of proteins remain unclassified. We have proposed an unsupervised machine-learning FuzzyART neural network algorithm to classify a given set of proteins into SCOP superfamilies. The proposed method is fast learning and uses an atypical non-linear pattern recognition technique. In this approach, we have constructed a similarity matrix from p-values of BLAST all-against-all, trained the network with FuzzyART unsupervised learning algorithm using the similarity matrix as input vectorsand finally the trained network offers SCOP superfamily level classification. In this experiment, we have evaluated the performance of our method with existing techniques on six different datasets. We have shown that the trained network is able to classify a given similarity matrix of a set of sequences into SCOP superfamilies at high classification accuracy.
机译：生物信息学的主要研究方向之一是在大型数据库中预测蛋白质超家族，并将给定的蛋白质结构域分类为超家族。分类反映了结构，进化和功能上的相关性。这些关系体现在层次分类中，例如手动组织的蛋白质结构分类（SCOP）。这种分类对于蛋白质的结构和功能分析至关重要。然而，大量蛋白质仍未分类。我们提出了一种无监督的机器学习FuzzyART神经网络算法，以将给定的一组蛋白质分类为SCOP超家族。所提出的方法是快速学习的，并且使用非典型的非线性模式识别技术。在这种方法中，我们从所有BLAST的所有p值构造了一个相似矩阵，使用FuzzyART无监督学习算法以相似矩阵作为输入向量对网络进行了训练，最后，训练后的网络提供了SCOP超家族等级分类。在此实验中，我们使用现有技术在六个不同的数据集上评估了该方法的性能。我们已经表明，训练有素的网络能够以高分类精度将一组序列的给定相似矩阵分类为SCOP超家族。

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