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MM-6mAPred: identifying DNA N6-methyladenine sites based on Markov model

https://doi.org/10.1093/bioinformatics/btz556
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/0005-2795(75)90109-9openalex-snapshot
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
inferredcites →https://doi.org/10.1016/0014-5793(83)80745-5openalex-snapshot
Cytosine modification in DNA by BcnI methylase yields N4‐methylcytosine
inferredcites →https://doi.org/10.1016/0022-5193(83)90251-5openalex-snapshot
A Markov analysis of DNA sequences
inferredcites →https://doi.org/10.1016/j.cell.2015.04.005openalex-snapshot
DNA Methylation on N6-Adenine in C. elegans
inferredcites →https://doi.org/10.1016/j.cell.2015.04.010openalex-snapshot
N6-Methyldeoxyadenosine Marks Active Transcription Start Sites in Chlamydomonas
inferredcites →https://doi.org/10.1016/j.cell.2015.04.018openalex-snapshot
N6-Methyladenine DNA Modification in Drosophila
inferredcites →https://doi.org/10.1016/j.molcel.2018.06.015openalex-snapshot
N6-Methyladenine DNA Modification in the Human Genome
inferredcites →https://doi.org/10.1016/j.molp.2017.10.003openalex-snapshot
Rice Information GateWay: A Comprehensive Bioinformatics Platform for Indica Rice Genomes
inferredcites →https://doi.org/10.1017/cbo9780511790492openalex-snapshot
Biological Sequence Analysis
inferredcites →https://doi.org/10.1038/nature17640openalex-snapshot
DNA methylation on N6-adenine in mammalian embryonic stem cells
inferredcites →https://doi.org/10.1038/ncomms13052openalex-snapshot
Abundant DNA 6mA methylation during early embryogenesis of zebrafish and pig
inferredcites →https://doi.org/10.1038/s41467-017-01195-yopenalex-snapshot
DNA N6-methyladenine is dynamically regulated in the mouse brain following environmental stress
inferredcites →https://doi.org/10.1038/s41477-018-0214-xopenalex-snapshot
Identification and analysis of adenine N6-methylation sites in the rice genome
inferredcites →https://doi.org/10.1089/cmb.1997.4.311openalex-snapshot
Improved Splice Site Detection in Genie
inferredcites →https://doi.org/10.1093/bioinformatics/15.5.362openalex-snapshot
Interpolated markov chains for eukaryotic promoter recognition.
inferredcites →https://doi.org/10.1093/bioinformatics/bti657openalex-snapshot
Markov model recognition and classification of DNA/protein sequences within large text databases
inferredcites →https://doi.org/10.1093/bioinformatics/btz015openalex-snapshot
i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome
inferredcites →https://doi.org/10.1093/nar/23.17.3554openalex-snapshot
Detection of new genes in a bacterial genome using Markov models for three gene classes
inferredcites →https://doi.org/10.1145/267521.267766openalex-snapshot
Improved splice site detection in Genie
inferredcites →https://doi.org/10.1515/bchm2.1966.344.1-3.280openalex-snapshot
Nachweis von Minoritätsbasen in Sperma-Desoxyribonucleinsäure

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An Evaluation of Multitask Transfer Learning Methods in Identifying 6mA and 5mC Methylation Sites of Rice and Maize
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Using k-mer embeddings learned from a Skip-gram based neural network for building a cross-species DNA N6-methyladenine site prediction model
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i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites
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MeDiCNet: Integrating Multi-scale Dynamic Convolution and Enhanced Position-Aware Transformer for DNA Methylation Site Prediction
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Identification of DNA modification sites based on elastic net and bidirectional gated recurrent unit with convolutional neural network
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The epigenetic roles of DNA N6-Methyladenine (6mA) modification in eukaryotes
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Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors
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CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction
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iResNetDM: An interpretable deep learning approach for four types of DNA methylation modification prediction
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AutoFE-Pointer: Auto-weighted feature extractor based on pointer network for DNA methylation prediction
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A Convolutional Neural Network Using Dinucleotide One-hot Encoder for identifying DNA N6-Methyladenine Sites in the Rice Genome
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Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes
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GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome
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Plant6mA: A predictor for predicting N6-methyladenine sites with lightweight structure in plant genomes
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Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species
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I-DNAN6mA: Accurate Identification of DNA N6-Methyladenine Sites Using the Base-Pairing Map and Deep Learning
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A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome
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Identifying the generator matrix of a stationary Markov chain using partially observable data
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Using Chou’s 5-steps rule to identify N6-methyladenine sites by ensemble learning combined with multiple feature extraction methods
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MMG4: Recognition of G4-Forming Sequences Based on Markov Model
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Prediction of bio-sequence modifications and the associations with diseases
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Critical evaluation of web-based DNA N6-methyladenine site prediction tools
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Meta-i6mA: an interspecies predictor for identifying DNAN6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework
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Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites
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BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches
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MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block
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R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting
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A review of methods for predicting DNA N6-methyladenine sites
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DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method
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6mA-Finder: a novel online tool for predicting DNA N6-methyladenine sites in genomes
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SOMM4mC: a second-order Markov model for DNA N4-methylcytosine site prediction in six species
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iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization
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i6mA-Caps: a CapsuleNet-based framework for identifying DNA N6-methyladenine sites
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Methyl-GP: accurate generic DNA methylation prediction based on a language model and representation learning
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SICD6mA: Identifying 6mA Sites using Deep Memory Network
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A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome
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Early Lung Cancer Detection Using Nucleotide Transition Probabilities in plasma cell-free DNA
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A Deep Learning Model for Predicting DNA N6-Methyladenine (6mA) Sites in Eukaryotes
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SpineNet-6mA: A Novel Deep Learning Tool for Predicting DNA N6-Methyladenine Sites in Genomes
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ZayyuNet – A Unified Deep Learning Model for the Identification of Epigenetic Modifications Using Raw Genomic Sequences
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Ense-i6mA: Identification of DNA N6-Methyladenine Sites Using XGB-RFE Feature Selection and Ensemble Machine Learning
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A deep learning approach to automate whole‐genome prediction of diverse epigenomic modifications in plants
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Prediction of DNA 6mA Methylation Sites Based on Bidirectional Long Short-Term Memory Network and Convolutional Neural Network
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Identification of DNA N6-methyladenine modifications in the rice genome with a fine-tuned large language model
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6mAPred-MSFF: A Deep Learning Model for Predicting DNA N6-Methyladenine Sites across Species Based on a Multi-Scale Feature Fusion Mechanism
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Additional file 1 of StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning
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HD-6mAPred: a hybrid deep learning approach for accurate prediction of N6-methyladenine sites in plant species
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