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A deep dense inception network for protein beta-turn prediction.
Proteins: Structure, Function, and Bioinformatics ( IF 2.9 ) Pub Date : 2019-07-23 , DOI: 10.1002/prot.25780
Chao Fang 1 , Yi Shang 1 , Dong Xu 1, 2
Affiliation  

Beta-turn prediction is useful in protein function studies and experimental design. Although recent approaches using machine-learning techniques such as support vector machine (SVM), neural networks, and K nearest neighbor have achieved good results for beta-turn prediction, there is still significant room for improvement. As previous predictors utilized features in a sliding window of 4-20 residues to capture interactions among sequentially neighboring residues, such feature engineering may result in incomplete or biased features and neglect interactions among long-range residues. Deep neural networks provide a new opportunity to address these issues. Here, we proposed a deep dense inception network (DeepDIN) for beta-turn prediction, which takes advantage of the state-of-the-art deep neural network design of dense networks and inception networks. A test on a recent BT6376 benchmark data set shows that DeepDIN outperformed the previous best tool BetaTPred3 significantly in both the overall prediction accuracy and the nine-type beta-turn classification accuracy. A tool, called MUFold-BetaTurn, was developed, which is the first beta-turn prediction tool utilizing deep neural networks. The tool can be downloaded at http://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html.

中文翻译:

一个深层密集的起始网络,用于预测蛋白质的β转换。

Beta转向预测可用于蛋白质功能研究和实验设计。尽管最近使用支持向量机(SVM),神经网络和K最近邻等机器学习技术的方法在beta转向预测中取得了良好的结果,但仍有很大的改进空间。由于先前的预测变量利用4-20个残基的滑动窗口中的特征来捕获顺序相邻的残基之间的相互作用,因此此类特征工程可能会导致特征不完整或有偏差,而忽略了远程残基之间的相互作用。深度神经网络为解决这些问题提供了新的机会。在这里,我们提出了用于β转弯预测的深层密集起始网络(DeepDIN),它利用了密集网络和起始网络的最新深度神经网络设计。在最近的BT6376基准数据集上进行的测试表明,DeepDIN在总体预测准确性和九种类型的beta-turn分类准确度方面均明显优于以前的最佳工具BetaTPred3。开发了一种称为MUFold-BetaTurn的工具,这是第一个利用深度神经网络的beta转向预测工具。可以从http://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html下载该工具。
更新日期:2019-12-09
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