mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method

Sushil Shakyawar, Siddesh Southekal, Chittibabu Guda

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Identification of miRNA–mRNA interactions is critical to understand the new paradigms in gene regulation. Existing methods show suboptimal performance owing to inappropriate feature selection and limited integration of intuitive biological features of both miRNAs and mRNAs. The present regularized least square-based method, mintRULS, employs features of miRNAs and their target sites using pairwise similarity metrics based on free energy, sequence and repeat identities, and target site accessibility to predict miRNA-target site interactions. We hypothesized that miRNAs sharing similar structural and functional features are more likely to target the same mRNA, and conversely, mRNAs with similar features can be targeted by the same miRNA. Our prediction model achieved an impressive AUC of 0.93 and 0.92 in LOOCV and LmiTOCV settings, respectively. In comparison, other popular tools such as miRDB, TargetScan, MBSTAR, RPmirDIP, and STarMir scored AUCs at 0.73, 0.77, 0.55, 0.84, and 0.67, respectively, in LOOCV setting. Similarly, mintRULS outperformed other methods using metrics such as accuracy, sensitivity, specificity, and MCC. Our method also demonstrated high accuracy when validated against experimentally derived data from condition- and cell-specific studies and expression studies of miRNAs and target genes, both in human and mouse.

Original languageEnglish (US)
Article number1528
JournalGenes
Volume13
Issue number9
DOIs
StatePublished - Sep 2022

Keywords

  • least square regression
  • miRNA–target site interaction
  • nucleotide sequence feature
  • pairwise feature scoring

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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