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RNA

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Exploring functional conservation in silico: a new machine learning approach to RNA-editing.

Briefings in bioinformatics
Around 50 years ago, molecular biology opened the path to understand changes in forms, adaptations, complexity, or the basis of human diseases through myriads of reports on gene birth, gene duplication, gene expression regulation, and splicing regula...

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification.

Molecular oncology
Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-lear...

m5c-iDeep: 5-Methylcytosine sites identification through deep learning.

Methods (San Diego, Calif.)
5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventiona...

pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data.

Life science alliance
High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologie...

Exploring the roles of RNAs in chromatin architecture using deep learning.

Nature communications
Recent studies have highlighted the impact of both transcription and transcripts on 3D genome organization, particularly its dynamics. Here, we propose a deep learning framework, called AkitaR, that leverages both genome sequences and genome-wide RNA...

Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction.

Journal of chemical information and modeling
N-7methylguanosine (m7G) modification plays a crucial role in various biological processes and is closely associated with the development and progression of many cancers. Accurate identification of m7G modification sites is essential for understandin...

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.

Nature communications
As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a ...

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions.

Nucleic acids research
Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web...

A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction.

Journal of chemical information and modeling
The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence...

m5C-Seq: Machine learning-enhanced profiling of RNA 5-methylcytosine modifications.

Computers in biology and medicine
Epigenetic modifications, particularly RNA methylation and histone alterations, play a crucial role in heredity, development, and disease. Among these, RNA 5-methylcytosine (m5C) is the most prevalent RNA modification in mammalian cells, essential fo...