Prediction of the miRNA interactome – Established methods and upcoming perspectives
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Author / Producer
Date
2020
Publication Type
Review Article
ETH Bibliography
yes
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Abstract
MicroRNAs (miRNAs) are well-studied small noncoding RNAs involved in post-transcriptional gene regulation in a wide range of organisms, including mammals. Their function is mediated by base pairing with their target RNAs. Although many features required for miRNA-mediated repression have been described, the identification of functional interactions is still challenging. In the last two decades, numerous Machine Learning (ML) models have been developed to predict their putative targets. In this review, we summarize the biological knowledge and the experimental data used to develop these ML models. Recently, Deep Neural Network-based models have also emerged in miRNA interaction modeling. We thus outline established and emerging models to give a perspective on the future developments needed to improve the identification of genes directly regulated by miRNAs.
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Publication status
published
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Book title
Journal / series
Volume
18
Pages / Article No.
548 - 557
Publisher
Research Network of Computational and Structural Biotechnology
Event
Edition / version
Methods
Software
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Date collected
Date created
Subject
Machine learning; Deep learning; microRNA target prediction
Organisational unit
03983 - Ciaudo, C. (ehemalig) / Ciaudo, C. (former)
Notes
Funding
173120 - Canonical and non-canonical functions of RNA interference proteins during mouse early development (SNF)