AIMC Topic: RNA

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ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing.

Nucleic acids research
RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanos...

A Hyperbolic Discrete Diffusion 3D RNA Inverse Folding Model for Functional RNA Design.

Journal of chemical information and modeling
Generative design of functional RNAs presents revolutionary opportunities for diverse RNA-based biotechnologies and biomedical applications. To this end, RNA inverse folding is a promising strategy for generatively designing new RNA sequences that ca...

m6A-SPP: Identification of RNA N6-methyladenosine modification sites through multi-source biological features and a hybrid deep learning architecture.

International journal of biological macromolecules
The N6-methyladenosine(m6A) modification plays crucial regulatory roles in various biological processes including gene expression regulation, RNA stability, splicing, and translation. Accurate prediction of m6A modification sites is essential for und...

DeepRNA-Twist: language-model-guided RNA torsion angle prediction with attention-inception network.

Briefings in bioinformatics
RNA torsion and pseudo-torsion angles are critical in determining the three-dimensional conformation of RNA molecules, which in turn governs their biological functions. However, current methods are limited by RNA's structural complexity as well as fl...

DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.

Nucleic acids research
Ribonucleic Acid (RNA) is the central conduit for information transfer in the cell. Identifying potential RNA targets in disease conditions is a challenging task, given the vast repertoire of functional non-coding RNAs in a human cell. A potential dr...

BAMBI integrates biostatistical and artificial intelligence methods to improve RNA biomarker discovery.

Briefings in bioinformatics
RNA biomarkers enable early and precise disease diagnosis, monitoring, and prognosis, facilitating personalized medicine and targeted therapeutic strategies. However, identification of RNA biomarkers is hindered by the challenge of analyzing relative...

DRAG: design RNAs as hierarchical graphs with reinforcement learning.

Briefings in bioinformatics
The rapid development of RNA vaccines and therapeutics puts forward intensive requirements on the sequence design of RNAs. RNA sequence design, or RNA inverse folding, aims to generate RNA sequences that can fold into specific target structures. To d...

The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.

Methods in molecular biology (Clifton, N.J.)
Nucleic acid tests (NATs) are considered as gold standard in molecular diagnosis. To meet the demand for onsite, point-of-care, specific and sensitive, trace and genotype detection of pathogens and pathogenic variants, various types of NATs have been...

Generative Modeling of RNA Sequence Families with Restricted Boltzmann Machines.

Methods in molecular biology (Clifton, N.J.)
In this chapter, we discuss the potential application of Restricted Boltzmann machines (RBM) to model sequence families of structured RNA molecules. RBMs are a simple two-layer machine learning model able to capture intricate sequence dependencies in...

gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Methods in molecular biology (Clifton, N.J.)
Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse p...