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Adenosine

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Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.

International journal of molecular sciences
N6-methyladenosine (mA) is the most abundant within eukaryotic messenger RNA modification, which plays an essential regulatory role in the control of cellular functions and gene expression. However, it remains an outstanding challenge to detect mRNA ...

Reduced response to regadenoson with increased weight: An artificial intelligence-based quantitative myocardial perfusion study.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: There is conflicting evidence regarding the response to a fixed dose of regadenoson in patients with high body weight. The aim of this study was to evaluate the effectiveness of regadenoson in patients with varying body weights using nove...

Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues.

Methods (San Diego, Calif.)
N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection ov...

BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs.

Molecules (Basel, Switzerland)
m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications...

Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data.

Methods (San Diego, Calif.)
With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devot...

m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq.

Nucleic acids research
We present m6ACali, a novel machine-learning framework aimed at enhancing the accuracy of N6-methyladenosine (m6A) epitranscriptome profiling by reducing the impact of non-specific antibody enrichment in MeRIP-Seq. The calibration model serves as a g...

Application of m6A regulators to predict transformation from myelodysplastic syndrome to acute myeloid leukemia via machine learning.

Medicine
Myelodysplastic syndrome (MDS) frequently transforms into acute myeloid leukemia (AML). Predicting the risk of its transformation will help to make the treatment plan. Levels of expression of N6-methyladenosine (m6A) regulators is difference in patie...

Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection.

Nature communications
We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usuall...

Deep learning based method for predicting DNA N6-methyladenosine sites.

Methods (San Diego, Calif.)
DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intens...