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

Wei Chen, Shaowu Zhang, Yong-mei Cheng, Q. Pan
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引用次数: 1

Abstract

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.
基于空间信息的SVM-KNN算法预测蛋白质- rna相互作用位点
蛋白质-RNA相互作用对许多基本的细胞过程至关重要,包括基因表达的调控,如RNA剪接、转运和翻译、蛋白质合成和核糖体的组装。更详细地了解蛋白质- rna相互作用有助于理解其功能符号和分子调控机制,同时,了解蛋白质- rna识别的知识也有助于生物科学家和研究人员理解定点诱变和药物设计。在本工作中,我们提出了一种基于SVM-KNN算法的计算方法,利用空间邻近残基的进化信息来预测蛋白质- rna相互作用位点。5倍交叉验证的总体成功率为78.00%,与现有的其他方法相当或更好,表明我们的方法在蛋白质- rna相互作用位点的鉴定和预测方面非常有前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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