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Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383879114> ?p ?o ?g. }

• W4383879114 endingPage "107242" @default.
• W4383879114 startingPage "107242" @default.
• W4383879114 abstract "MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in regulating gene expression at the post-transcriptional level by binding to potential target sites of messenger RNAs (mRNAs), facilitated by the Argonaute family of proteins. Selecting the conservative candidate target sites (CTS) is a challenging step, considering that most of the existing computational algorithms primarily focus on canonical site types, which is a time-consuming and inefficient utilization of miRNA target site interactions. We developed a stacking classifier algorithm that addresses the CTS selection criteria using feature-encoding techniques that generates feature vectors, including k-mer nucleotide composition, dinucleotide composition, pseudo-nucleotide composition, and sequence order coupling. This innovative stacking classifier algorithm surpassed previous state-of-the-art algorithms in predicting functional miRNA targets. We evaluated the performance of the proposed model on 10 independent test datasets and obtained an average accuracy of 79.77%, which is a significant improvement of 7.26 % over previous models. This improvement shows that the proposed method has great potential for distinguishing highly functional miRNA targets and can serve as a valuable tool in biomedical and drug development research." @default.
• W4383879114 created "2023-07-12" @default.
• W4383879114 creator A5008953921 @default.
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• W4383879114 date "2023-09-01" @default.
• W4383879114 modified "2023-10-17" @default.
• W4383879114 title "An ensemble of stacking classifiers for improved prediction of miRNA–mRNA interactions" @default.
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• W4383879114 doi "https://doi.org/10.1016/j.compbiomed.2023.107242" @default.
• W4383879114 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37473564" @default.
• W4383879114 hasPublicationYear "2023" @default.
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