AIMC Topic: Adenosine

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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...
Algorithms Computational Biology Methylation HEK293 Cells Adenosine RNA, Circular Humans Neural Networks, Computer
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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 Humans Machine Learning Algorithms RNA Processing, Post-Transcriptional Nanopore Sequencing RNA Methylation Methylation HEK293 Cells Adenosine
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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...
Humans Adenosine Angina, Unstable Coronary Stenosis Reproducibility of Results Risk Factors Aged Male Middle Aged Severity of Illness Index Prognosis Retrospective Studies Female Artificial Intelligence Algorithms Coronary Circulation Myocardial Perfusion Imaging Finland Coronary Vessels Vasodilator Agents Computed Tomography Angiography Time Factors Predictive Value of Tests Coronary Angiography Coronary Artery Disease Positron-Emission Tomography
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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...
Animals Deep Learning Computational Biology RNA, Messenger Adenosine Humans Neural Networks, Computer
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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 Transcriptome Adenosine 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 ...
Computational Biology Mammals RNA Adenosine Reproducibility of Results Animals Deep Learning
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Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species.

Methods (San Diego, Calif.) Apr 25, 2022
DNA N6-methyladenine (6mA) is a key DNA modification, which plays versatile roles in the cellular processes, including regulation of gene expression, DNA repair, and DNA replication. DNA 6mA is closely associated with many diseases in the mammals and...
Deep Learning DNA Methylation Mammals DNA Adenosine Animals
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m6A modification: recent advances, anticancer targeted drug discovery and beyond.

Molecular cancer Feb 14, 2022
Abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer, and aberrant m6A regulators have been identified as novel anticancer drug targets. Both traditional medicine-r...
Artificial Intelligence Prognosis Neoplasms Humans Drug Discovery Adenosine
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Identification of Synovial Fibroblast-Associated Neuropeptide Genes and m6A Factors in Rheumatoid Arthritis Using Single-Cell Analysis and Machine Learning.

Disease markers Feb 9, 2022
OBJECTIVES: Synovial fibroblasts (SFs) play an important role in the development and progression of rheumatoid arthritis (RA). However, the pathogenic mechanism of SFs remains unclear. The objective of this study was to investigate how neuropeptides ...
Machine Learning Single-Cell Analysis Neuropeptides Arthritis, Rheumatoid Synovial Membrane Fibroblasts Adenosine Humans
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bCNN-Methylpred: Feature-Based Prediction of RNA Sequence Modification Using Branch Convolutional Neural Network.

Genes Jul 28, 2021
RNA modification is vital to various cellular and biological processes. Among the existing RNA modifications, N-methyladenosine (m6A) is considered the most important modification owing to its involvement in many biological processes. The prediction ...
Sequence Analysis, RNA Adenosine Neural Networks, Computer Computational Biology
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