DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites is a research paper published in Briefings in Bioinformatics (2020). On theSindex it has a DataRank of 0. It has been cited 131 times.
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National Health and Medical Research Council of Australia
Grant: 1092262
NHMRC Program in Cellular Microbiology
Australian Research Council
Grant: DP120104460
Stochastic modelling of telomere length regulation in ageing research
Australian Research Council
Grant: LP110200333
Characterisation of plant cysteine proteases with therapeutic potential
National Institute of Allergy and Infectious Diseases
Grant: R01 AI111965
National Institutes of Health
Grant: 5R01AI111965-04
New Tricks for 'Old' Drugs: PK/PD of Polymyxin Nonantibiotic Combinations
National Institutes of Health
Monash University; Collaborative Research Program of Institute for Chemical Research, Kyoto University
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Sustainable Development Goals
Additional file 1 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction
Additional file 1 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction
Additional file 2 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction
Additional file 2 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction
Additional file 3 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction
Additional file 3 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction