AIMC Topic: Adenosine

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A combined deep learning framework for mammalian m6A site prediction.

Cell genomics Nov 20, 2024
N-methyladenosine (m6A) is the most prevalent chemical modification in eukaryotic mRNAs and plays key roles in diverse cellular processes. Precise localization of m6A sites is thus critical for characterizing the functional roles of m6A in various co...
Humans Computational Biology RNA, Messenger Neural Networks, Computer Mammals Animals Adenosine Deep Learning
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Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection.

Nature communications Aug 21, 2024
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...
Animals RNA, Messenger Escherichia coli Adenosine Humans Saccharomyces cerevisiae Nanopore Sequencing Machine Learning RNA, Transfer
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Deep learning based method for predicting DNA N6-methyladenosine sites.

Methods (San Diego, Calif.) Aug 6, 2024
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...
DNA Methylation DNA Adenosine Humans Deep Learning Computational Biology
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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 Jul 25, 2024
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...
Coronary Circulation Risk Factors Myocardial Perfusion Imaging Aged Humans Adenosine Male Adenosine A2 Receptor Agonists Middle Aged Pyrazoles Artificial Intelligence Predictive Value of Tests Vasodilator Agents Body Weight Case-Control Studies Algorithms Coronary Artery Disease Purines Image Interpretation, Computer-Assisted Female Logistic Models Obesity
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BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs.

Molecules (Basel, Switzerland) May 21, 2024
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...
Methylation HEK293 Cells Adenosine RNA, Circular Neural Networks, Computer Algorithms Computational Biology Humans
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Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data.

Methods (San Diego, Calif.) May 18, 2024
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...
Sequence Analysis, RNA Workflow RNA Methylation Humans Machine Learning Algorithms Nanopore Sequencing RNA Methylation HEK293 Cells Adenosine RNA Processing, Post-Transcriptional
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Prognostic value of a novel artificial intelligence-based coronary CTA-derived ischemia algorithm among patients with normal or abnormal myocardial perfusion.

Journal of cardiovascular computed tomography Apr 24, 2024
BACKGROUND: Among patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA), downstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemi...
Coronary Circulation Myocardial Perfusion Imaging Finland Adenosine Angina, Unstable Coronary Stenosis Coronary Vessels Vasodilator Agents Computed Tomography Angiography Coronary Angiography Coronary Artery Disease Positron-Emission Tomography Risk Factors Aged Male Time Factors Predictive Value of Tests Middle Aged Severity of Illness Index Humans Female Prognosis Artificial Intelligence Reproducibility of Results Retrospective Studies Algorithms
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Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues.

Methods (San Diego, Calif.) Mar 12, 2024
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...
Humans Neural Networks, Computer Animals RNA, Messenger Adenosine Deep Learning Computational Biology
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DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical function in a variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently ...
Animals Adenosine Transcriptome Deep Learning Mammals
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Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.

International journal of molecular sciences Dec 7, 2022
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 ...
Adenosine Computational Biology Mammals RNA Reproducibility of Results Animals Deep Learning
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