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- W2557808169 abstract "MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org." @default.
- W2557808169 created "2016-12-08" @default.
- W2557808169 creator A5012268284 @default.
- W2557808169 creator A5017006322 @default.
- W2557808169 creator A5047384190 @default.
- W2557808169 date "2016-11-28" @default.
- W2557808169 modified "2023-10-12" @default.
- W2557808169 title "miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA–target interaction data" @default.
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- W2557808169 doi "https://doi.org/10.1093/nar/gkw1185" @default.
- W2557808169 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5389546" @default.
- W2557808169 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27903911" @default.
- W2557808169 hasPublicationYear "2016" @default.
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