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Representation of non-coding RNA-mediated regulation of gene expression using the Gene Ontology.

RNA biology
Regulatory non-coding RNAs (ncRNAs) are increasingly recognized as integral to the control of biological processes. This is often through the targeted regulation of mRNA expression, but this is by no means the only mechanism through which regulatory ...

BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.

RNA biology
The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various...

NPI-DCGNN: An Accurate Tool for Identifying ncRNA-Protein Interactions Using a Dual-Channel Graph Neural Network.

Journal of computational biology : a journal of computational molecular cell biology
Noncoding RNA (NcRNA)-protein interactions (NPIs) play fundamentally important roles in carrying out cellular activities. Although various predictors based on molecular features and graphs have been published to boost the identification of NPIs, most...

Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.

EBioMedicine
The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers....

Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers.

International journal of molecular sciences
Curated online interaction databases and gene ontology tools have streamlined the analysis of highly complex gene/protein networks. However, understanding of disease pathogenesis has gradually shifted from a protein-based core to complex interactive ...

Comparison and benchmark of deep learning methods for non-coding RNA classification.

PLoS computational biology
The involvement of non-coding RNAs in biological processes and diseases has made the exploration of their functions crucial. Most non-coding RNAs have yet to be studied, creating the need for methods that can rapidly classify large sets of non-coding...

High throughput variant libraries and machine learning yield design rules for retron gene editors.

Nucleic acids research
The bacterial retron reverse transcriptase system has served as an intracellular factory for single-stranded DNA in many biotechnological applications. In these technologies, a natural retron non-coding RNA (ncRNA) is modified to encode a template fo...

DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network.

Briefings in bioinformatics
Non-coding RNAs (ncRNAs) play crucial roles in drug resistance and sensitivity, making them important biomarkers and therapeutic targets. However, predicting ncRNA-drug associations is challenging due to issues such as dataset imbalance and sparsity,...

MMnc: multi-modal interpretable representation for non-coding RNA classification and class annotation.

Bioinformatics (Oxford, England)
MOTIVATION: As the biological roles and disease implications of non-coding RNAs continue to emerge, the need to thoroughly characterize previously unexplored non-coding RNAs becomes increasingly urgent. These molecules hold potential as biomarkers an...

Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis.

BMC cancer
Non-coding RNAs (ncRNAs) play a crucial role in breast cancer progression, necessitating advanced computational approaches for precise disease classification. This study introduces a Deep Reinforcement Learning (DRL)-based framework for predicting nc...