Prediction of RNA-Binding Proteins by VotingSystems

[摘要] It is important to identify which proteins can interact with RNA for the purpose ofprotein annotation, since interactions between RNA and proteins influence thestructure of the ribosome and play important roles in gene expression. This papertries to identify proteins that can interact with RNA using voting systems. Firstlythrough Weka, 34 learning algorithms are chosen for investigation. Then simplemajority voting system (SMVS) is used for the prediction of RNA-binding proteins,achieving average ACC (overall prediction accuracy) value of 79.72% and MCC(Matthew’s correlation coefficient) value of 59.77% for theindependent testing dataset. Then mRMR (minimum redundancy maximum relevance)strategy is used, which is transferred into algorithm selection. In addition, theMCC value of each classifier is assigned to be the weight of theclassifier’s vote. As a result, best average MCC values are attainedwhen 22 algorithms are selected and integrated through weighted votes, which are64.70% for the independent testing dataset, and ACC value is 82.04% at thismoment.

[学科分类] 基础医学