As one of the widespread RNA post-transcriptional modifications (PTCMs), 5-Methylcytosine (m5C) click here plays vital roles in better understanding of basic biological mechanisms and major disease treatments.In experiments, traditional high-throughput approaches to find m5C sites are usually expensive and laborious.Additionally, facing with a large number of RNA sequences, developing accurate computational methods to distinguish m5C and non-m5C sites is an efficient solution.Here we introduced a novel predictor, called iRNA-m5C_NB, to identify m5C sites in Home sapiens using Naive Bayes (NB) algorithm.
In this method, unbalanced dataset Met935 is firstly analyzed using efficient hybrid-sampling strategy SMOTEEEN.Then top 57 features are selected by the ANOVA F-value from four kinds of well-performance feature extraction techniques, including Bi-profile Bayes (BPB), enhanced Nucleic Acid Composition (ENAC), electron-ion interaction pseudopotentials (EIIP) and mMGap_1.Based on the jackknife test, the evaluated recall for the unbalanced training dataset Met935 is up to 82.81% with MCC of 0.
63.And for the independent dataset Test1157, the predictor still shows high recall of 70.06% and MCC of 0.34.
It is the first m5C predictor constructed using the unbalanced dataset, and the recall scores are increased by 19.82% and 59.23% for jackknife and independent tests compared with the latest tool beer button down shirts for men RNAm5CPred, respectively.We demonstrate that the proposed predictor iRNA-m5C_NB outperforms other state-of-art models, which hopes to be an efficient and reliable method to identify m5C sites.