About: Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks
Creator	Hopkins, Mark Maslov, Sergei Heflin, Maeve Liu, Simon Nambiar, Ananthan »more»
source	BioRxiv
abstract	The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-trains task-agnostic sequence representations. This model is fine-tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction. Our method is comparable to existing state-of-the art approaches for protein family classification, while being much more general than other architectures. Further, our method outperforms all other approaches for protein interaction prediction. These results offer a promising framework for fine-tuning the pre-trained sequence representations for other protein prediction tasks.
has issue date	2020-06-16(xsd:dateTime)
bibo:doi	10.1101/2020.06.15.153643
has license	biorxiv
sha1sum (hex)	5f4c7f404e2e9d6b16fa91f93b2bf628367bfd5c
schema:url	https://doi.org/10.1101/2020.06.15.153643
resource representing a document's title	Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks
schema:publication	bioRxiv
resource representing a document's body	covid:5f4c7f404e2e9d6b16fa91f93b2bf628367bfd5c#body_text
is schema:about of	named entity 'deep learning' named entity 'MUCH' named entity 'THESE' named entity 'SEQUENCE' named entity 'PRE' »more»

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