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首页> 外文期刊>International Journal of Intelligent Information Technologies >Combining Artificial Neural Networks and GOR-V Information Theory to Predict Protein Secondary Structure from Amino Acid Sequences
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Combining Artificial Neural Networks and GOR-V Information Theory to Predict Protein Secondary Structure from Amino Acid Sequences

机译：结合人工神经网络和GOR-V信息论从氨基酸序列预测蛋白质二级结构

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摘要

Protein secondary structure prediction is a fundamental step in determining the 3D structure of a protein. In this paper, a new method for predicting protein secondary structure from amino acid sequences has been proposed and implemented. Cuff and Barton 513 protein data set is used in training and testing the prediction methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GOR-V information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary structure classes (helices, strands, and coils). The newly developed method (NN-GORV-I) is improved further by applying a filtering mechanism to the searched database and, hence, named NN-GORV-II. The developed prediction methods are rigorously analyzed and tested, together with other five well-known prediction methods in this domain in order to allow easy comparison and clear conclusions.
机译：蛋白质二级结构预测是确定蛋白质3D结构的基本步骤。本文提出并实现了一种从氨基酸序列预测蛋白质二级结构的新方法。 Cuff和Barton 513蛋白质数据集用于在相同的硬件，平台和环境下训练和测试预测方法。新开发的方法利用了GOR-V信息理论的知识和神经网络的强大功能，将一种新型蛋白质序列分为三个二级结构类别（螺旋，链和线圈）之一。通过对搜索的数据库应用过滤机制，进一步改进了新开发的方法（NN-GORV-I），因此将其命名为NN-GORV-II。所开发的预测方法将与该领域中的其他五种知名预测方法一起进行严格的分析和测试，以便于进行比较和得出清晰的结论。

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