AIMC Topic: RNA

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Detecting N-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines.

Scientific reports Jan 12, 2017
As one of the most abundant RNA post-transcriptional modifications, N-methyladenosine (mA) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. Howe...
Adenosine RNA Processing, Post-Transcriptional Support Vector Machine Transcriptome RNA Computational Biology Saccharomyces cerevisiae
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A Graph Approach to Mining Biological Patterns in the Binding Interfaces.

Journal of computational biology : a journal of computational molecular cell biology Nov 28, 2016
Protein-RNA interactions play important roles in the biological systems. Searching for regular patterns in the Protein-RNA binding interfaces is important for understanding how protein and RNA recognize each other and bind to form a complex. Herein, ...
Humans Animals Molecular Docking Simulation Support Vector Machine Proteins Data Mining Protein Binding Computational Biology RNA-Binding Proteins Protein Interaction Domains and Motifs Binding Sites Computer Graphics Amino Acid Motifs RNA Protein Conformation
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TargetM6A: Identifying N-Methyladenosine Sites From RNA Sequences via Position-Specific Nucleotide Propensities and a Support Vector Machine.

IEEE transactions on nanobioscience Aug 10, 2016
As one of the most ubiquitous post-transcriptional modifications of RNA, N-methyladenosine ( [Formula: see text]) plays an essential role in many vital biological processes. The identification of [Formula: see text] sites in RNAs is significantly imp...
Saccharomyces cerevisiae Adenosine Computational Biology Sequence Analysis, RNA Support Vector Machine RNA
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Extending gene ontology in the context of extracellular RNA and vesicle communication.

Journal of biomedical semantics Apr 12, 2016
BACKGROUND: To address the lack of standard terminology to describe extracellular RNA (exRNA) data/metadata, we have launched an inter-community effort to extend the Gene Ontology (GO) with subcellular structure concepts relevant to the exRNA domain....
Web Browser RNA Molecular Sequence Annotation Gene Ontology Extracellular Vesicles Databases, Genetic Humans
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PRIdictor: Protein-RNA Interaction predictor.

Bio Systems Dec 1, 2015
Several computational methods have been developed to predict RNA-binding sites in protein, but its inverse problem (i.e., predicting protein-binding sites in RNA) has received much less attention. Furthermore, most methods that predict RNA-binding si...
Proteins Amino Acids RNA Binding Sites Ribonucleotides Computational Biology Support Vector Machine
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GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies.

PloS one Aug 27, 2015
Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is...
RNA Folding Knowledge Bases Algorithms RNA Models, Molecular Game Theory
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Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression.

Journal of biomolecular NMR Jul 4, 2015
The Biological Magnetic Resonance Data Bank (BMRB) contains NMR chemical shift depositions for over 200 RNAs and RNA-containing complexes. We have analyzed the (1)H NMR and (13)C chemical shifts reported for non-exchangeable protons of 187 of these R...
Data Mining Nucleotides RNA Databases as Topic Information Storage and Retrieval Support Vector Machine Carbon Hydrogen Automation Regression Analysis Calibration
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A semi-supervised learning approach for RNA secondary structure prediction.

Computational biology and chemistry Feb 20, 2015
RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because o...
Nucleic Acid Conformation Supervised Machine Learning RNA Stochastic Processes
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Computationally predicting protein-RNA interactions using only positive and unlabeled examples.

Journal of bioinformatics and computational biology Feb 8, 2015
Protein-RNA interactions (PRIs) are considerably important in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulations of gene expression to the active defense of host against virus. With the development...
Databases, Protein RNA Saccharomyces cerevisiae Escherichia coli Bone Substitutes Humans Animals Drosophila melanogaster Proteins Caenorhabditis elegans Mice Protein Interaction Mapping Support Vector Machine
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m6A-SPP: Identification of RNA N6-methyladenosine modification sites through multi-source biological features and a hybrid deep learning architecture.

International journal of biological macromolecules Jun 1, 2025
The N6-methyladenosine(m6A) modification plays crucial regulatory roles in various biological processes including gene expression regulation, RNA stability, splicing, and translation. Accurate prediction of m6A modification sites is essential for und...
Humans Neural Networks, Computer Deep Learning Methylation HEK293 Cells Adenosine Computational Biology RNA HeLa Cells
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