Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives
<p dir="ltr">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 th...
محفوظ في:
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| مؤلفون آخرون: | , |
| منشور في: |
2020
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| _version_ | 1864513513958932480 |
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| author | Tanvir Alam (638619) |
| author2 | Hamada R. H. Al-Absi (16726299) Sebastian Schmeier (215058) |
| author2_role | author author |
| author_facet | Tanvir Alam (638619) Hamada R. H. Al-Absi (16726299) Sebastian Schmeier (215058) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tanvir Alam (638619) Hamada R. H. Al-Absi (16726299) Sebastian Schmeier (215058) |
| dc.date.none.fl_str_mv | 2020-11-30T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/ncrna6040047 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_Learning_in_LncRNAome_Contribution_Challenges_and_Perspectives/25907611 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Information and computing sciences Machine learning long non-coding RNA lncRNA lncRNAome deep learning machine learning convolutional neural network CNN LSTM Attention mechanism |
| dc.title.none.fl_str_mv | Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Non-Coding RNA<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/ncrna6040047" target="_blank">https://dx.doi.org/10.3390/ncrna6040047</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_937f17aa5a6cabceee0ccb2dd20c43af |
| identifier_str_mv | 10.3390/ncrna6040047 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25907611 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep Learning in LncRNAome: Contribution, Challenges, and PerspectivesTanvir Alam (638619)Hamada R. H. Al-Absi (16726299)Sebastian Schmeier (215058)Biological sciencesBioinformatics and computational biologyInformation and computing sciencesMachine learninglong non-coding RNAlncRNAlncRNAomedeep learningmachine learningconvolutional neural networkCNNLSTMAttention mechanism<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Non-Coding RNA<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/ncrna6040047" target="_blank">https://dx.doi.org/10.3390/ncrna6040047</a></p>2020-11-30T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/ncrna6040047https://figshare.com/articles/journal_contribution/Deep_Learning_in_LncRNAome_Contribution_Challenges_and_Perspectives/25907611CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/259076112020-11-30T06:00:00Z |
| spellingShingle | Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives Tanvir Alam (638619) Biological sciences Bioinformatics and computational biology Information and computing sciences Machine learning long non-coding RNA lncRNA lncRNAome deep learning machine learning convolutional neural network CNN LSTM Attention mechanism |
| status_str | publishedVersion |
| 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 | Biological sciences Bioinformatics and computational biology Information and computing sciences Machine learning long non-coding RNA lncRNA lncRNAome deep learning machine learning convolutional neural network CNN LSTM Attention mechanism |