基于旋转森林和自相关描述符的蛋白质-蛋白质相互作用的序列预测
Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor.
作者信息
Xia Jun-Feng, Han Kyungsook, Huang De-Shuang
机构信息
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China.
出版信息
Protein Pept Lett. 2010 Jan;17(1):137-45. doi: 10.2174/092986610789909403.
We propose a sequence-based multiple classifier system, i.e., rotation forest, to infer protein-protein interactions (PPIs). Moreover, Moran autocorrelation descriptor is used to code an interaction protein pair. Experimental results on Saccharomyces cerevisiae and Helicobacter pylori datasets show that our approach outperforms those previously published in literature, which demonstrates the effectiveness of the proposed method.
我们提出了一种基于序列的多分类器系统，即旋转森林，用于推断蛋白质-蛋白质相互作用（PPI）。此外，使用莫兰自相关描述符对相互作用的蛋白质对进行编码。在酿酒酵母和幽门螺杆菌数据集上的实验结果表明，我们的方法优于文献中先前发表的方法，这证明了所提方法的有效性。
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