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A Decision Tree-Based Method for Protein Contact Map Prediction

机译:基于决策树的蛋白质接触图预测方法

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In this paper, we focus on protein contact map prediction. We describe a method where contact maps are predicted using decision tree-based model. The algorithm includes the subsequence information between the couple of analyzed amino acids. In order to evaluate the method generalization capabilities, we carry out an experiment using 173 non-homologous proteins of known structures. Our results indicate that the method can assign protein contacts with an average accuracy of 0.34, superior to the 0.25 obtained by the FNETCSS method. This shows that our algorithm improves the accuracy with respect to the methods compared, especially with the increase of protein length.
机译:在本文中,我们专注于蛋白质接触图谱预测。我们描述了一种使用基于决策树的模型预测联系图的方法。该算法包括两个已分析氨基酸之间的子序列信息。为了评估方法的通用能力,我们使用173种已知结构的非同源蛋白质进行了实验。我们的结果表明,该方法可以分配蛋白质接触的平均精度为0.34,优于FNETCSS方法获得的0.25。这表明我们的算法相对于所提方法提高了准确性,尤其是随着蛋白质长度的增加。

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