ProQ3D：使用深度学习改进模型质量评估。
ProQ3D: improved model quality assessments using deep learning.
作者信息
Uziela Karolis, Menéndez Hurtado David, Shu Nanjiang, Wallner Björn, Elofsson Arne
机构信息
Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.
Bioinformatics Short-term Support and Infrastructure (BILS), Science for Life Laboratory, Solna, Sweden.
出版信息
Bioinformatics. 2017 May 15;33(10):1578-1580. doi: 10.1093/bioinformatics/btw819.
SUMMARY
Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features).
AVAILABILITY AND IMPLEMENTATION
ProQ3D is freely available both as a webserver and a stand-alone program at http://proq3.bioinfo.se/.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
蛋白质质量评估是生物信息学中一个长期存在的问题。十多年来，我们通过精心选择和优化机器学习方法的输入，开发了先进的预测器。相关性已从ProQ中的0.60提高到ProQ2中的0.81和ProQ3中的0.85，主要是通过添加大量经过精心调整的蛋白质描述。在此，我们表明，使用与ProQ2或ProQ3完全相同的输入，但将支持向量机替换为深度神经网络，可以取得实质性的改进。这将皮尔逊相关性提高到了0.90（使用ProQ2输入特征时为0.85）。
可用性和实现方式
ProQ3D可作为网络服务器和独立程序在http://proq3.bioinfo.se/上免费获取。
联系方式
补充信息
补充数据可在《生物信息学》在线获取。
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