regel-corpus/hunflair-tfbs

HunFlair model for Transcription Factor Binding Site (TFBS)

HunFlair (biomedical flair) for TFBS entity.

Predicts 1 tag:

tag	meaning
Tfbs	DNA region bound by transcription factor

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Cite

Please cite the following paper when using this model.

@article{garda2022regel,
  title={RegEl corpus: identifying DNA regulatory elements in the scientific literature},
  author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf},
  journal={Database},
  volume={2022},
  year={2022},
  publisher={Oxford Academic}
}

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Demo: How to use in Flair

Requires:

• Flair (pip install flair)

from flair.data import Sentence
from flair.models import SequenceTagger
# for biomedical-specific tokenization:
# from flair.tokenization import SciSpacyTokenizer

# load tagger
tagger = SequenceTagger.load("regel-corpus/hunflair-tfbs")

text = "We found that Egr-1 specifically binds to the PTEN 5' untranslated region, which contains a functional GCGGCGGCG Egr-1-binding site."

# make example sentence
sentence = Sentence(text)

# for biomedical-specific tokenization:
# sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer())

# predict NER tags
tagger.predict(sentence)

# print sentence
print(sentence)

# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
    print(entity)

This yields the following output:

Span [19,20,21]: "GCGGCGGCG Egr-1-binding site"   [− Labels: Tfbs (0.9631)]

So, the entity "GCGGCGGCG Egr-1-binding site" is found in the sentence.

Alternatively download all models locally and use the MultiTagger class.

from flair.models import MultiTagger

tagger = [
'./models/hunflair-promoter/pytorch_model.bin',
'./models/hunflair-enhancer/pytorch_model.bin',
'./models/hunflair-tfbs/pytorch_model.bin',
]

tagger = MultiTagger.load(['./models/hunflair-'])

tagger.predict(sentence)

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