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Prediction of protein disordered regions in a protein sequence based on gap-constraint subsequence patterns

机译:基于间隙约束子序列模式的蛋白质序列中蛋白质紊乱区域预测

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The disordered region is an important protein structure which contains much information about protein function. Until now, the prediction of protein disordered region is also much popular task. In this paper, we proposed a new approach to predict protein disordered regions in a protein sequence using the gap-constraint subsequence patterns mining and association rule mining. At first, the gap-constraint frequent subsequences are generated by Gap-BIDE algorithm in two classes, disordered sequence and ordered sequence. Based on these frequent subsequences, we calculated the conditional probability of disordered subsequence patterns in both classes and classify the candidates into the class which has the higher conditional probability. Finally, we used the disordered/ordered subsequence patterns which we generate to search the disordered regions in a protein sequence. In the experiment, we used the CASP 9 and Disprot 5.7 dataset as test data and the performance is higher than other methods.
机译:无序区域是包含有关蛋白质功能的许多信息的重要蛋白质结构。到目前为止,蛋白质无序区域的预测也是多么受欢迎的任务。在本文中,我们提出了一种新方法,使用差距约束子序列挖掘和关联规则挖掘来预测蛋白质序列中蛋白质紊乱区域。首先,间隙约束频繁子句是由两个类,无序序列和有序序列中的间隙 - 竞争算法生成的。基于这些频繁的子序列,我们计算了两种类中的无序子序列模式的条件概率,并将候选者分类为具有较高条件概率的类别。最后,我们使用了我们生成的无序/有序的子序列模式,以在蛋白质序列中搜索无序区域。在实验中,我们使用Casp 9和DISprot 5.7数据集作为测试数据,性能高于其他方法。

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