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Prediction of Protein-RNA interaction site using SVM-KNN algorithm with spatial information

机译:使用SVM-KNN算法预测具有空间信息的蛋白质-RNA相互作用位点

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Protein-RNA interactions are vitally important to a number of fundamental cellular processes, including regulation of gene expression such as RNA splicing, transport and translation, protein synthesis and assembly of ribosome. More detailed information on the Protein-RNA interaction is helpful for comprehending the function notation and molecular regulatory mechanism, meanwhile, knowing the knowledge of Protein-RNA recognition can also help the biological scientist and researcher understand the site-directed mutagenesis and drug design. In the present work, we proposed a computational approach, based on SVM-KNN algorithm, with evolutionary information of spatial neighbour residues for prediction of protein-RNA interaction sites. The overall success rate obtained by 5-fold cross-validation is 78.00%, which is comparable or better than other existing methods, indicating our method is very promising for identifying and predicting protein-RNA interaction sites.
机译:蛋白质-RNA相互作用对许多基本细胞过程至关重要,包括调节基因表达,例如RNA剪接,转运和翻译,蛋白质合成和核糖体组装。有关蛋白质-RNA相互作用的更多详细信息有助于理解功能标记和分子调控机制,同时,了解蛋白质-RNA识别知识也可以帮助生物学家和研究人员了解定点诱变和药物设计。在当前的工作中,我们提出了一种基于SVM-KNN算法的计算方法,该方法利用空间相邻残基的进化信息来预测蛋白质-RNA相互作用位点。通过5倍交叉验证获得的总成功率为78.00%,与其他现有方法相当或更好,这表明我们的方法在鉴定和预测蛋白质-RNA相互作用位点方面非常有前途。

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