DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome is a research paper published in Bioinformatics (2021). On theSindex it has a DataRank of 0. It has been cited 1,276 times.
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National Institutes of Health
Grant: R01LM011297
National Library of Medicine
FWCI
46.63
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords
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Additional file 1 of CVD-associated SNPs with regulatory potential reveal novel non-coding disease genes
Additional file 2 of Context dependency of nucleotide probabilities and variants in human DNA
Additional file 2 of Context dependency of nucleotide probabilities and variants in human DNA
Additional file 1 of Supervised promoter recognition: a benchmark framework
Additional file 1 of Supervised promoter recognition: a benchmark framework
Additional file 1 of iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations
Additional file 1 of iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations
Additional file 2 of iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations
Additional file 2 of iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations
Additional file 1 of Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences
Additional file 1 of Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences
Additional file 2 of Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences
Additional file 2 of Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences
Additional file 3 of Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences
Additional file 3 of Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences
Additional file 1 of DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network
Additional file 1 of DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network
Additional file 2 of DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network
Additional file 2 of DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network