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References from Machine learning applications in RNA modification sites prediction. Local targets link to admitted publications; unresolved targets remain external evidence.
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Repic: A database for exploring the n 6-methyladenosine methylome
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Unresolved reference
External reference
m7ghub: deciphering the location, regulation and pathogenesis of internal mrna n7-methylguanosine (m7g) sites in human
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10.1186/s12859-019-3265-8
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An interpretable prediction model for identifying n7-methylguanosine sites based on xgboost and shap
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Cnn-mgp: Convolutional neural networks for metagenomics gene prediction, Interdisciplinary Sciences: Computational
2019 · External reference
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Unresolved reference
External reference
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10.1186/s13040-018-0170-z · 2018 · External reference
Cnn-mgp: Convolutional neural networks for metagenomics gene prediction, Interdisciplinary Sciences: Computational
2019 · External reference
10.1145/1143844.1143951
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A method for avoiding bias from feature selection with application to naive Bayes classification models
2008 · External reference
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10.1038/mtna.2016.37 · 2016 · External reference
Unresolved reference
2016 · External reference
10.2174/1573406413666170623082245
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10.1016/j.jtbi.2018.12.034
10.1016/j.jtbi.2018.12.034 · External reference
Deepmrmp: A new predictor for multiple types of rna modification sites using deep learning
10.3934/mbe.2019310 · 2019 · External reference
10.1016/j.ab.2015.08.021
10.1016/j.ab.2015.08.021 · External reference
Identification and analysis of the n6-methyladenosine in the saccharomyces cerevisiae transcriptome
2015 · External reference
methyladenosine sites in the Arabidopsis thaliana transcriptome
10.1007/s00438-016-1243-7 · 2016 · External reference
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m6a reader: Epitranscriptome target prediction and functional characterization of n6-methyladenosine (m6a) readers
10.3389/fcell.2020.00741 · 2020 · External reference
10.1016/j.omtn.2018.07.004
10.1016/j.omtn.2018.07.004 · External reference
M6amrfs: Robust prediction of n6-methyladenosine sites with sequence-based features in multiple species
10.3389/fgene.2018.00495 · 2018 · External reference
10.1261/rna.069112.118
10.1261/rna.069112.118 · External reference
10.1016/j.chemolab.2019.103811
10.1016/j.chemolab.2019.103811 · External reference
Deepm6aseq: Prediction and characterization of m6a-containing sequences using deep learning
10.1186/s12859-018-2516-4 · 2018 · External reference
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10.1093/nar/gkw104
10.1093/nar/gkw104 · External reference
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10.1007/s11103-018-0698-9 · 2018 · External reference
10.1089/cmb.2018.0004
10.1089/cmb.2018.0004 · External reference
Unresolved reference
2018 · External reference
Metdiff: A novel differential rna methylation analysis for merip-seq data
10.1109/tcbb.2015.2403355 · 2018 · External reference
Prediction of n7-methylguanosine sites in human rna based on optimal sequence features
10.1016/j.ygeno.2020.07.035 · 2020 · External reference
10.1016/j.molcel.2019.03.036
10.1016/j.molcel.2019.03.036 · External reference
m7gpredictor: An improved machine learning-based model for predicting internal m7g modifications using sequence properties
10.1016/j.ab.2020.113905 · 2020 · External reference
iRNAm 5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition
10.18632/oncotarget.17104 · 2017 · External reference
Transcriptome-wide annotation of m5c rna modifications using machine learning
10.3389/fpls.2018.00519 · 2018 · External reference
10.1016/j.omtn.2017.03.006
10.1016/j.omtn.2017.03.006 · External reference
Rnam5cpred: Prediction of rna 5-methylcytosine sites based on three different kinds of nucleotide composition
10.1016/j.omtn.2019.10.008 · 2019 · External reference
Prediction of m5c modifications in rna sequences by combining multiple sequence features
10.1016/j.omtn.2020.06.004 · 2020 · External reference
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10.1039/c6mb00471g · 2016 · External reference
m5cpred-svm: a novel method for predicting m5c sites of rna
10.1186/s12859-020-03828-4 · 2020 · External reference
irna5hmc: The first predictor to identify rna 5-hydroxymethylcytosine modifications using machine learning
10.3389/fbioe.2020.00227 · 2020 · External reference
Accurate prediction of rna 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors, Computational and Structural
2020 · External reference
Prediction of rna 5-hydroxymethylcytosine modifications using deep learning
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10.1109/access.2020.2991477 · External reference
Ppus: a web server to predict pus-specific pseudouridine sites: Table 1
10.1093/bioinformatics/btv366 · 2015 · External reference
10.2183/pjab.91.394
10.2183/pjab.91.394 · External reference
Ipseu-ncp: Identifying rna pseudouridine sites using random forest and ncp-encoded features
10.1186/s12864-019-6357-y · 2019 · External reference
Xg-pseu: an extreme gradient boosting based method for identifying pseudouridine sites
10.1007/s00438-019-01600-9 · 2020 · External reference
Ensempseu: Identifying pseudouridine sites with an ensemble approach
10.1109/access.2020.2989469 · 2020 · External reference
PSI-MOUSE: Predicting Mouse Pseudouridine Sites From Sequence and Genome-Derived Features
10.1177/1176934320925752 · 2020 · External reference
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10.1016/j.omtn.2019.03.010 · 2019 · External reference
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2020 · External reference
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2018 · External reference
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10.1109/access.2020.2991070 · 2020 · External reference
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10.3390/molecules24030380 · External reference
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Irna-ai: Identifying the adenosine to inosine editing sites in rna sequences
10.18632/oncotarget.13758 · 2017 · External reference
Epai-nc: Enhanced prediction of adenosine to inosine rna editing sites using nucleotide compositions
10.1016/j.ab.2019.01.002 · 2019 · External reference
irna-m2g: Identifying n2-methylguanosine sites based on sequence-derived information
10.1016/j.omtn.2019.08.023 · 2019 · External reference
imrm: a platform for simultaneously identifying multiple kinds of rna modifications
10.1093/bioinformatics/btaa155 · 2020 · External reference
10.1016/j.omtn.2018.03.012
10.1016/j.omtn.2018.03.012 · External reference
