AIMC Topic: RNA Processing, Post-Transcriptional

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SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.

PLoS computational biology Dec 11, 2018
LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational ...
Databases, Nucleic Acid Humans Base Sequence Databases, Protein RNA Processing, Post-Transcriptional Machine Learning Amino Acid Sequence RNA, Long Noncoding Computational Biology Protein Binding RNA-Binding Proteins Algorithms
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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...
RNA Processing, Post-Transcriptional RNA Adenosine Saccharomyces cerevisiae Computational Biology Transcriptome Support Vector Machine
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Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning.

Briefings in bioinformatics Sep 23, 2024
RNA 2'-O-methylation (Nm) is a crucial post-transcriptional modification with significant biological implications. However, experimental identification of Nm sites is challenging and resource-intensive. While multiple computational tools have been de...
Deep Learning Neural Networks, Computer Computational Biology Humans Methylation Software RNA Processing, Post-Transcriptional Bayes Theorem RNA
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Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.

Nucleic acids research Sep 20, 2021
N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based appr...
Sensitivity and Specificity Animals Machine Learning RNA, Messenger HEK293 Cells Mice RNA-Seq Humans RNA Processing, Post-Transcriptional Mouse Embryonic Stem Cells Nucleotide Motifs Methyltransferases Adenosine
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DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning.

Mathematical biosciences and engineering : MBE Jul 4, 2019
RNA modification plays an indispensable role in the regulation of organisms. RNA modification site prediction offers an insight into diverse cellular processing. Regarding different types of RNA modification site prediction, it is difficult to tell t...
RNA Processing, Post-Transcriptional 5-Methylcytosine Pseudouridine Humans Species Specificity Adenosine Mice Saccharomyces cerevisiae Deep Learning Algorithms RNA Animals Computational Biology Machine Learning
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