Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Long non-coding RNAs (lncRNAs) are increasingly recognized for their critical roles in diverse biological processes.
• Advancements in deep learning (DL) offer powerful new tools for biological data analysis.

Purpose of the Study:

• To provide a comprehensive review of the convergence between deep learning and long non-coding RNA research.
• To highlight the significance of DL methodologies in elucidating lncRNA functions.
• To offer insights into the latest DL applications in lncRNA studies from 2021-2023.

Main Methods:

• Systematic literature review of recent research (2021-2023).
• Analysis of deep learning techniques applied to long non-coding RNA investigation.
• Synthesis of findings on the role of DL in understanding lncRNA biology.

Main Results:

• Deep learning models are increasingly utilized to analyze lncRNA sequences, structures, and functions.
• DL applications are enhancing the prediction of lncRNA-protein interactions and regulatory networks.
• Recent studies demonstrate DL's capability in identifying novel lncRNAs and their disease associations.

Conclusions:

• The integration of deep learning with lncRNA research is a rapidly advancing and highly promising area.
• DL methodologies provide powerful approaches to unravel the complex roles of lncRNAs in biological systems.
• Researchers can leverage these DL advancements to deepen their understanding of lncRNA biology and its implications.