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Applications of Machine Learning in miRNA Discovery and Target Prediction

MicroRNA (miRNA) is a small non-coding molecule that is involved in gene regulation and RNA silencing by complementary on their targets. Experimental methods for target prediction can be time-consuming and expensive. Thus, the application of the computational approach is implicated to enlighten thes...

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Autores principales: Parveen, Alisha, Mustafa, Syed H., Yadav, Pankaj, Kumar, Abhishek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bentham Science Publishers 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7290058/
https://www.ncbi.nlm.nih.gov/pubmed/32581642
http://dx.doi.org/10.2174/1389202921666200106111813
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author Parveen, Alisha
Mustafa, Syed H.
Yadav, Pankaj
Kumar, Abhishek
author_facet Parveen, Alisha
Mustafa, Syed H.
Yadav, Pankaj
Kumar, Abhishek
author_sort Parveen, Alisha
collection PubMed
description MicroRNA (miRNA) is a small non-coding molecule that is involved in gene regulation and RNA silencing by complementary on their targets. Experimental methods for target prediction can be time-consuming and expensive. Thus, the application of the computational approach is implicated to enlighten these complications with experimental studies. However, there is still a need for an optimized approach in miRNA biology. Therefore, machine learning (ML) would initiate a new era of research in miRNA biology towards potential diseases biomarker. In this article, we described the application of ML approaches in miRNA discovery and target prediction with functions and future prospective. The implementation of a new era of computational methodologies in this direction would initiate further advanced levels of discoveries in miRNA.
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spelling pubmed-72900582020-06-23 Applications of Machine Learning in miRNA Discovery and Target Prediction Parveen, Alisha Mustafa, Syed H. Yadav, Pankaj Kumar, Abhishek Curr Genomics Article MicroRNA (miRNA) is a small non-coding molecule that is involved in gene regulation and RNA silencing by complementary on their targets. Experimental methods for target prediction can be time-consuming and expensive. Thus, the application of the computational approach is implicated to enlighten these complications with experimental studies. However, there is still a need for an optimized approach in miRNA biology. Therefore, machine learning (ML) would initiate a new era of research in miRNA biology towards potential diseases biomarker. In this article, we described the application of ML approaches in miRNA discovery and target prediction with functions and future prospective. The implementation of a new era of computational methodologies in this direction would initiate further advanced levels of discoveries in miRNA. Bentham Science Publishers 2019-12 2019-12 /pmc/articles/PMC7290058/ /pubmed/32581642 http://dx.doi.org/10.2174/1389202921666200106111813 Text en © 2019 Bentham Science Publishers https://creativecommons.org/licenses/by-nc/4.0/legalcode This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
spellingShingle Article
Parveen, Alisha
Mustafa, Syed H.
Yadav, Pankaj
Kumar, Abhishek
Applications of Machine Learning in miRNA Discovery and Target Prediction
title Applications of Machine Learning in miRNA Discovery and Target Prediction
title_full Applications of Machine Learning in miRNA Discovery and Target Prediction
title_fullStr Applications of Machine Learning in miRNA Discovery and Target Prediction
title_full_unstemmed Applications of Machine Learning in miRNA Discovery and Target Prediction
title_short Applications of Machine Learning in miRNA Discovery and Target Prediction
title_sort applications of machine learning in mirna discovery and target prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7290058/
https://www.ncbi.nlm.nih.gov/pubmed/32581642
http://dx.doi.org/10.2174/1389202921666200106111813
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