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• W2799691204 abstract "Non-coding RNA molecules play essential roles by interacting with other molecules to perform various biological functions. However, it is difficult to determine RNA structures due to their flexibility. At present, the number of experimentally solved RNA-ligand and RNA-protein structures is still insufficient. Therefore, binding sites prediction of non-coding RNA is required to understand their functions.Current RNA binding site prediction algorithms produce many false positive nucleotides that are distance away from the binding sites. Here, we present a network approach, RBind, to predict the RNA binding sites. We benchmarked RBind in RNA-ligand and RNA-protein datasets. The average accuracy of 0.82 in RNA-ligand and 0.63 in RNA-protein testing showed that this network strategy has a reliable accuracy for binding sites prediction.The codes and datasets are available at https://zhaolab.com.cn/RBind.Supplementary data are available at Bioinformatics online." @default.
• W2799691204 created "2018-05-17" @default.
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• W2799691204 date "2018-04-26" @default.
• W2799691204 modified "2023-10-10" @default.
• W2799691204 title "RBind: computational network method to predict RNA binding sites" @default.
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• W2799691204 doi "https://doi.org/10.1093/bioinformatics/bty345" @default.
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• W2799691204 hasPublicationYear "2018" @default.
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