Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives

• Main Authors: Tanvir Alam, Hamada R. H. Al-Absi, Sebastian Schmeier
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Non-Coding RNA
• Subjects: long non-coding RNA; lncRNA; lncRNAome; deep learning; machine learning; convolutional neural network
• Online Access: https://www.mdpi.com/2311-553X/6/4/47

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.