LncRNA-Top : Controlled Deep Learning Approaches for LncRNA Gene Regulatory Relationship Annotations across Different Platforms

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Detail(s)

Original languageEnglish
Article number108197
Journal / PublicationiScience
Volume26
Issue number11
Online published12 Oct 2023
Publication statusPublished - 17 Nov 2023

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Abstract

By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs. © 2023 The Author(s).

Research Area(s)

  • lncRNA, lncRNA-Gene, Controlled Deep-Learning, ensemble methods

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