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Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives

1
College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar
2
School of Natural and Computational Sciences, Massey University, Auckland 0632, New Zealand
*
Author to whom correspondence should be addressed.
Non-Coding RNA 2020, 6(4), 47; https://doi.org/10.3390/ncrna6040047
Received: 25 July 2020 / Revised: 27 October 2020 / Accepted: 6 November 2020 / Published: 30 November 2020
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. View Full-Text
Keywords: long non-coding RNA; lncRNA; lncRNAome; deep learning; machine learning; convolutional neural network; CNN; LSTM; Attention mechanism long non-coding RNA; lncRNA; lncRNAome; deep learning; machine learning; convolutional neural network; CNN; LSTM; Attention mechanism
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MDPI and ACS Style

Alam, T.; Al-Absi, H.R.H.; Schmeier, S. Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives. Non-Coding RNA 2020, 6, 47. https://doi.org/10.3390/ncrna6040047

AMA Style

Alam T, Al-Absi HRH, Schmeier S. Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives. Non-Coding RNA. 2020; 6(4):47. https://doi.org/10.3390/ncrna6040047

Chicago/Turabian Style

Alam, Tanvir; Al-Absi, Hamada R.H.; Schmeier, Sebastian. 2020. "Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives" Non-Coding RNA 6, no. 4: 47. https://doi.org/10.3390/ncrna6040047

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