Corpus ID: 226278128

Is Transfer Learning Necessary for Protein Landscape Prediction?

@article{Shanehsazzadeh2020IsTL,
  title={Is Transfer Learning Necessary for Protein Landscape Prediction?},
  author={Amir Shanehsazzadeh and D. Belanger and David Dohan},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.03443}
}
  • Amir Shanehsazzadeh, D. Belanger, David Dohan
  • Published 2020
  • Computer Science, Biology
  • ArXiv
  • Recently, there has been great interest in learning how to best represent proteins, specifically with fixed-length embeddings. Deep learning has become a popular tool for protein representation learning as a model's hidden layers produce potentially useful vector embeddings. TAPE introduced a number of benchmark tasks and showed that semi-supervised learning, via pretraining language models on a large protein corpus, improved performance on downstream tasks. Two of the tasks (fluorescence… CONTINUE READING
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