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Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives
Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened numerous avenues for the research community regardin...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711891/ https://www.ncbi.nlm.nih.gov/pubmed/33266128 http://dx.doi.org/10.3390/ncrna6040047 |
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author | Alam, Tanvir Al-Absi, Hamada R. H. Schmeier, Sebastian |
author_facet | Alam, Tanvir Al-Absi, Hamada R. H. Schmeier, Sebastian |
author_sort | Alam, Tanvir |
collection | PubMed |
description | Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened numerous avenues for the research community regarding lncRNAome. To discover and understand lncRNAome, many sophisticated computational techniques have been leveraged. Recently, deep learning (DL)-based modeling techniques have been successfully used in genomics due to their capacity to handle large amounts of data and produce relatively better results than traditional machine learning (ML) models. DL-based modeling techniques have now become a choice for many modeling tasks in the field of lncRNAome as well. In this review article, we summarized the contribution of DL-based methods in nine different lncRNAome research areas. We also outlined DL-based techniques leveraged in lncRNAome, highlighting the challenges computational scientists face while developing DL-based models for lncRNAome. To the best of our knowledge, this is the first review article that summarizes the role of DL-based techniques in multiple areas of lncRNAome. |
format | Online Article Text |
id | pubmed-7711891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77118912020-12-04 Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives Alam, Tanvir Al-Absi, Hamada R. H. Schmeier, Sebastian Noncoding RNA Review Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened numerous avenues for the research community regarding lncRNAome. To discover and understand lncRNAome, many sophisticated computational techniques have been leveraged. Recently, deep learning (DL)-based modeling techniques have been successfully used in genomics due to their capacity to handle large amounts of data and produce relatively better results than traditional machine learning (ML) models. DL-based modeling techniques have now become a choice for many modeling tasks in the field of lncRNAome as well. In this review article, we summarized the contribution of DL-based methods in nine different lncRNAome research areas. We also outlined DL-based techniques leveraged in lncRNAome, highlighting the challenges computational scientists face while developing DL-based models for lncRNAome. To the best of our knowledge, this is the first review article that summarizes the role of DL-based techniques in multiple areas of lncRNAome. MDPI 2020-11-30 /pmc/articles/PMC7711891/ /pubmed/33266128 http://dx.doi.org/10.3390/ncrna6040047 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Alam, Tanvir Al-Absi, Hamada R. H. Schmeier, Sebastian Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives |
title | Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives |
title_full | Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives |
title_fullStr | Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives |
title_full_unstemmed | Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives |
title_short | Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives |
title_sort | deep learning in lncrnaome: contribution, challenges, and perspectives |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711891/ https://www.ncbi.nlm.nih.gov/pubmed/33266128 http://dx.doi.org/10.3390/ncrna6040047 |
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