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RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction

MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predictin...

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Autores principales: Kyrollos, Daniel G., Reid, Bradley, Dick, Kevin, Green, James R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366700/
https://www.ncbi.nlm.nih.gov/pubmed/32678114
http://dx.doi.org/10.1038/s41598-020-68251-4
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author Kyrollos, Daniel G.
Reid, Bradley
Dick, Kevin
Green, James R.
author_facet Kyrollos, Daniel G.
Reid, Bradley
Dick, Kevin
Green, James R.
author_sort Kyrollos, Daniel G.
collection PubMed
description MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predicting which miRNA will target which gene. Application of these predictors to large numbers of unique miRNA–gene pairs has resulted in datasets comprising tens of millions of scored interactions; the largest among these is mirDIP. We here demonstrate that miRNA target prediction can be significantly improved ([Formula: see text] ) through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction. The RP method, aptly named RPmirDIP, augments the original mirDIP prediction scores by leveraging local thresholds from the two complimentary views available to each miRNA–gene pair, rather than apply a traditional global decision threshold. Application of this novel RPmirDIP predictor promises to help identify new, unexpected miRNA–gene interactions. A dataset of RPmirDIP-scored interactions are made available to the scientific community at cu-bic.ca/RPmirDIP and 10.5683/SP2/LD8JKJ.
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spelling pubmed-73667002020-07-17 RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction Kyrollos, Daniel G. Reid, Bradley Dick, Kevin Green, James R. Sci Rep Article MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predicting which miRNA will target which gene. Application of these predictors to large numbers of unique miRNA–gene pairs has resulted in datasets comprising tens of millions of scored interactions; the largest among these is mirDIP. We here demonstrate that miRNA target prediction can be significantly improved ([Formula: see text] ) through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction. The RP method, aptly named RPmirDIP, augments the original mirDIP prediction scores by leveraging local thresholds from the two complimentary views available to each miRNA–gene pair, rather than apply a traditional global decision threshold. Application of this novel RPmirDIP predictor promises to help identify new, unexpected miRNA–gene interactions. A dataset of RPmirDIP-scored interactions are made available to the scientific community at cu-bic.ca/RPmirDIP and 10.5683/SP2/LD8JKJ. Nature Publishing Group UK 2020-07-16 /pmc/articles/PMC7366700/ /pubmed/32678114 http://dx.doi.org/10.1038/s41598-020-68251-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kyrollos, Daniel G.
Reid, Bradley
Dick, Kevin
Green, James R.
RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction
title RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction
title_full RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction
title_fullStr RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction
title_full_unstemmed RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction
title_short RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction
title_sort rpmirdip: reciprocal perspective improves mirna targeting prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366700/
https://www.ncbi.nlm.nih.gov/pubmed/32678114
http://dx.doi.org/10.1038/s41598-020-68251-4
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