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Prediction of Protein Structural Class by Functional Link Artificial Neural Network Using Hybrid Feature Extraction Method

机译:混合特征提取的功能链接人工神经网络预测蛋白质结构分类

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During last few decades' accurate prediction of protein structural class has been a challenging problem. Efficient and meaningful representation of protein molecule plays a significant role. In this paper Chou's pseudo amino acid composition along with amphiphillic correlation factor and the spectral characteristics of the protein has been used to represent protein data. Thus a protein sample is represented by a set of discrete components which incorporate both the sequence order and the sequence length effects. On the basis of such a statistical framework a simple functionally linked artificial neural network has been used for structural class prediction.
机译:在过去的几十年中,对蛋白质结构分类的准确预测一直是一个具有挑战性的问题。蛋白质分子的有效和有意义的表达起着重要的作用。在本文中,Chou的伪氨基酸组成与两亲相关因子以及蛋白质的光谱特征已被用来表示蛋白质数据。因此,蛋白质样品由一组离散的成分代表,这些成分结合了序列顺序和序列长度效应。在这种统计框架的基础上,简单的功能链接的人工神经网络已用于结构类别的预测。

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