RNA functions by interacting with its intended targets structurally. However, due to the
dynamic nature of RNA molecules, RNA structures are difficult to determine experimentally …

Foundation models (FMs) are large-scale deeplearning models that are developed using
large datasets and self-supervised learning methods. These models serve as a base for …

Precise estimations of RNA secondary structures have the potential to reveal the various
roles that non-coding RNAs play in regulating cellular activity. However, the mainstay of …

Messenger RNA (mRNA) vaccines have emerged as a powerful tool against communicable
diseases and cancers, as demonstrated by their huge success during the coronavirus …

RNA molecules play a crucial role in various biological processes, with their functionality
closely tied to their structures. The remarkable advancements in machine learning …

A mixture-of-experts (MoE) approach has been developed to mitigate the poor out-of-
distribution (OOD) generalization of deep learning (DL) models for single-sequence-based …

The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell
than its tertiary structure, making it essential for functional prediction. Although deep learning …

RNA-protein interactions (RPI) are ubiquitous in cellular organisms and essential for gene
regulation. In particular, protein interactions with non-coding RNAs (ncRNAs) play a critical …

The design of RNAs that fulfill desired functions is one of the major challenges in
computational biology. The function of an RNA molecule depends on its structure and a …

RNA-protein interactions (RPI) are ubiquitous in cellular organisms and essential for gene
regulation. In particular, protein interactions with non-coding RNAs (ncRNAs) play a critical …