Research graph
References from DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species. Local targets link to admitted publications; unresolved targets remain external evidence.
Selective recognition of n 4-methylcytosine in dna by engineered transcription-activator-like effectors
10.1098/rstb.2017.0078 · 2018 · External reference
N4-cytosine dna methylation is involved in the maintenance of genomic stability in deinococcus radiodurans
10.3389/fmicb.2019.01905 · 2019 · External reference
Current review on dna methylation in ovarian cancer
2012 · External reference
10.1590/s0100-879x2005001000010
10.1590/s0100-879x2005001000010 · External reference
10.1038/sj.onc.1205651
10.1038/sj.onc.1205651 · External reference
10.1038/nrg1655
10.1038/nrg1655 · External reference
10.1016/0959-440x(95)80003-j
10.1016/0959-440x(95)80003-j · External reference
10.1016/j.devcel.2018.03.012
10.1016/j.devcel.2018.03.012 · External reference
10.1002/bies.20342
10.1002/bies.20342 · External reference
Methsmrt: an integrative database for dna n6-methyladenine and n4-methylcytosine generated by single-molecular real-time sequencing
2016 · External reference
10.1038/nrg.2017.80
10.1038/nrg.2017.80 · External reference
10.1038/nrg2341
10.1038/nrg2341 · External reference
10.1016/j.cell.2015.04.021
10.1016/j.cell.2015.04.021 · External reference
Exploring sequence-based features for the improved prediction of dna n4-methylcytosine sites in multiple species
10.1093/bioinformatics/bty824 · 2019 · External reference
10.2144/000112807
10.2144/000112807 · External reference
10.1101/gr.083451.108
10.1101/gr.083451.108 · External reference
10.1038/nmeth.1459
10.1038/nmeth.1459 · External reference
10.1093/nar/gku1097
10.1093/nar/gku1097 · External reference
Base-resolution detection of n 4-methylcytosine in genomic dna using 4mc-tet-assisted-bisulfite-sequencing
2015 · External reference
Novel architecture with selected feature vector for effective classification of mitotic and non-mitotic cells in breast cancer histology images
10.1016/j.bspc.2021.103212 · 2022 · External reference
idna4mc: identifying dna n4-methylcytosine sites based on nucleotide chemical properties
10.1093/bioinformatics/btx479 · 2017 · External reference
4mcpred: machine learning methods for dna n4-methylcytosine sites prediction
10.1093/bioinformatics/bty668 · 2019 · External reference
10.1093/bioinformatics/btz408
10.1093/bioinformatics/btz408 · External reference
Deeptorrent: a deep learning-based approach for predicting dna n4-methylcytosine sites
10.1093/bib/bbaa124 · 2021 · External reference
4mccnn: Identification of n4-methylcytosine sites in prokaryotes using convolutional neural network
10.1109/access.2019.2943169 · 2019 · External reference
Developing a multi-layer deep learning based predictive model to identify dna n4-methylcytosine modifications
10.3389/fbioe.2020.00274 · 2020 · External reference
A deep neural network for identifying dna n4-methylcytosine sites
10.3389/fgene.2020.00209 · 2020 · External reference
4mcpred-el: an ensemble learning framework for identification of dna n4-methylcytosine sites in the mouse genome
10.3390/cells8111332 · 2019 · External reference
i4mc-rose, a bioinformatics tool for the identification of dna n4-methylcytosine sites in the rosaceae genome
10.1016/j.ijbiomac.2019.12.009 · 2020 · External reference
idna-ms: an integrated computational tool for detecting dna modification sites in multiple genomes
10.1016/j.isci.2020.100991 · 2020 · External reference
10.1038/s41591-018-0316-z
10.1038/s41591-018-0316-z · External reference
Mdr: an integrative dna n6-methyladenine and n4-methylcytosine modification database for rosaceae
2019 · External reference
Dna6ma-mint: Dna-6ma modification identification neural tool
10.3390/genes11080898 · 2020 · External reference
Spinenet-6ma: a novel deep learning tool for predicting dna n6-methyladenine sites in genomes
10.1109/access.2020.3036090 · 2020 · External reference
10.1109/access.2020.3002995
10.1109/access.2020.3002995 · External reference
Cr-prom: A convolutional neural network-based model for the prediction of rice promoters
2021 · External reference
Unresolved reference
2016 · External reference
The matthews correlation coefficient (mcc) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
10.1186/s13040-021-00244-z · 2021 · External reference
The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation
10.1186/s12864-019-6413-7 · 2020 · External reference
Visualizing data using t-sne
2008 · External reference
