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Methylation

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A brief review of machine learning methods for RNA methylation sites prediction.

Methods (San Diego, Calif.)
Thanks to the tremendous advancement of deep sequencing and large-scale profiling, epitranscriptomics has become a rapidly growing field. As one of the most important parts of epitranscriptomics, ribonucleic acid (RNA) methylation has been focused on...

EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction.

BMC bioinformatics
BACKGROUND: Recent research recommends that epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all sorts of RNA. Exact identification of RNA modification is vital for understanding their purposes and regulato...

A three-methylation-driven gene-based deep learning model for tuberculosis diagnosis in patients with and without human immunodeficiency virus co-infection.

Microbiology and immunology
Improved diagnostic tests for tuberculosis (TB) among people with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methylation-driven genes (MDGs) of host blood could be used to diagnose patients co-infected with HIV/TB....

Deep learning modeling mA deposition reveals the importance of downstream cis-element sequences.

Nature communications
The N-methyladenosine (mA) modification is deposited to nascent transcripts on chromatin, but its site-specificity mechanism is mostly unknown. Here we model the mA deposition to pre-mRNA by iM6A (intelligent mA), a deep learning method, demonstratin...

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences.

Briefings in bioinformatics
2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety...

HSM6AP: a high-precision predictor for the Homo N6-methyladenosine (m^6 A) based on multiple weights and feature stitching.

RNA biology
Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information ...

EDLmAPred: ensemble deep learning approach for mRNA mA site prediction.

BMC bioinformatics
BACKGROUND: As a common and abundant RNA methylation modification, N6-methyladenosine (mA) is widely spread in various species' transcriptomes, and it is closely related to the occurrence and development of various life processes and diseases. Thus, ...

DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins.

Molecular omics
Methylation, which is one of the most prominent post-translational modifications on proteins, regulates many important cellular functions. Though several model-based methylation site predictors have been reported, all existing methods employ machine ...

iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm.

Genes
One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequen...

Identification of 2'-O-methylation Site by Investigating Multi-feature Extracting Techniques.

Combinatorial chemistry & high throughput screening
BACKGROUND: RNA methylation is a reversible post-transcriptional modification involving numerous biological processes. Ribose 2'-O-methylation is part of RNA methylation. It has shown that ribose 2'-O-methylation plays an important role in immune rec...