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

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 l...

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Detalles Bibliográficos
Autores principales: Shakyawar, Sushil, Southekal, Siddesh, Guda, Chittibabu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498445/
https://www.ncbi.nlm.nih.gov/pubmed/36140696
http://dx.doi.org/10.3390/genes13091528
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author Shakyawar, Sushil
Southekal, Siddesh
Guda, Chittibabu
author_facet Shakyawar, Sushil
Southekal, Siddesh
Guda, Chittibabu
author_sort Shakyawar, Sushil
collection PubMed
description 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.
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spelling pubmed-94984452022-09-23 mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method Shakyawar, Sushil Southekal, Siddesh Guda, Chittibabu Genes (Basel) Article 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. MDPI 2022-08-25 /pmc/articles/PMC9498445/ /pubmed/36140696 http://dx.doi.org/10.3390/genes13091528 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shakyawar, Sushil
Southekal, Siddesh
Guda, Chittibabu
mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method
title mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method
title_full mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method
title_fullStr mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method
title_full_unstemmed mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method
title_short mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method
title_sort mintruls: prediction of mirna–mrna target site interactions using regularized least square method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498445/
https://www.ncbi.nlm.nih.gov/pubmed/36140696
http://dx.doi.org/10.3390/genes13091528
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