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• W2462807123 abstract "N6-methyladenosine (m6A) is present ubiquitously in the RNA of living organisms from Escherichia coli to humans. Nonetheless, the exact molecular mechanism of this modification remains unclear. The experimental identification of m6A modification is time-consuming and expensive; therefore, bioinformatics tools with high accuracy represent desirable alternatives for the large-scale, rapid identification of N6-methyladenosine sites. In this study, RNA-MethylPred, a new bioinformatics model, was developed by incorporating bi-profile Bayes, dinucleotide composition, and k nearest neighbor (KNN) scores for three feature extractions. RNA-MethylPred yielded a Matthew's correlation coefficient (MCC) of 0.53 in a jackknife test, which was 0.24 higher than that of iRNA-Methyl and 0.13 higher than that of pRNAm-PC. The obvious improvements demonstrated that RNA-MethylPred might be a powerful and complementary tool for further experimental investigation of N6-methyladenosine modification." @default.
• W2462807123 created "2016-07-22" @default.
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• W2462807123 date "2016-10-01" @default.
• W2462807123 modified "2023-10-16" @default.
• W2462807123 title "RNA-MethylPred: A high-accuracy predictor to identify N6-methyladenosine in RNA" @default.
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• W2462807123 doi "https://doi.org/10.1016/j.ab.2016.06.012" @default.
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