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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...

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Autores principales: Alam, Tanvir, Al-Absi, Hamada R. H., Schmeier, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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.
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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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