The exponential growth of big data in RNA biology (RB) has led to the development of deep
learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL …

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 …

RNA is a crucial regulator in living organisms and malfunctions can lead to severe diseases.
To explore RNA-based therapeutics and applications, computational structure prediction …

Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in
the regulation of RNA expression and function. Accurate identification of RNA modification …

This review paper provides an extensive analysis of the rapidly evolving convergence of
deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements …

The lack of well-structured metadata annotations complicates the reusability and
interpretation of the growing amount of publicly available RNA expression data. The …

Making no use of physical laws or co-evolutionary information, de novo deep learning (DL)
models for RNA secondary structure prediction have achieved far superior performances …

The current deluge of newly identified RNA transcripts presents a singular opportunity for
improved assessment of coding potential, a cornerstone of genome annotation, and for …

Extracting inherent valuable knowledge from omics big data remains as a daunting problem
in bioinformatics and computational biology. Deep learning, as an emerging branch from …

Long non-coding RNAs (lncRNAs), comprising a significant portion of the human
transcriptome, serve as vital regulators of cellular processes and potential disease …