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A method for predicting RNA-protein interaction and interaction sites

机译:预测RNA-蛋白质相互作用和相互作用位点的方法

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Given the sequences of an RNA and a protein as input, a biologist may wish to know whether or not the RNA-protein pair interact. If they interact, where are the interaction sites? Knowing the RNA binding sites often provide useful clues for the understanding of a variety of biological processes, developing the computational methods to address these questions can be really helpful. In this study, we use features including Pseudo Position-Specific Score Matrix (PsePSSM) computed by PSI-BLAST and Dipeptide Composition (DC) as feature vectors. Then, the Knearest neighbor (K-NN) and Support Vector Machine (SVM) classifiers are employed to identify the residues that interact with RNA in RNA-binding protein. Our experiments show that the above methods are used effectively to deal with this complicated problem of predicting RNA-protein interaction and interaction sites.
机译:给定RNA和蛋白质的序列作为输入,生物学家可能希望知道RNA-蛋白质对是否相互作用。如果他们进行交互,那么交互站点在哪里?知道RNA结合位点通常可以为理解各种生物学过程提供有用的线索,开发解决这些问题的计算方法确实很有帮助。在这项研究中,我们使用包括通过PSI-BLAST计算的伪特定位置评分矩阵(PsePSSM)和二肽成分(DC)作为特征向量的特征。然后,使用Knearest邻居(K-NN)和支持向量机(SVM)分类器来识别与RNA结合蛋白中的RNA相互作用的残基。我们的实验表明,上述方法可有效地解决预测RNA-蛋白质相互作用和相互作用位点这一复杂问题。

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