<p>ProteinBERT: A universal deep-learning model of protein sequence and function</p>

Dan Ofer; Nadav Brandes; Michal Linial; Nadav Rappoport; Yam Peleg

doi:10.7490/f1000research.1118643.1

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Ofer D, Brandes N, Linial M et al. ProteinBERT: A universal deep-learning model of protein sequence and function [version 1; not peer reviewed]. F1000Research 2021, 10(ISCB Comm J):647 (poster) (https://doi.org/10.7490/f1000research.1118643.1)

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ProteinBERT: A universal deep-learning model of protein sequence and function

Dan Ofer¹, Nadav Brandes¹, Michal Linial¹, Nadav Rappoport, Yam Peleg

Published 24 Jul 2021 (https://doi.org/10.7490/f1000research.1118643.1)

Author Affiliations

¹ Hebrew University of Jerusalem, Israel

Copyright © 2021 Ofer D et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 24 Jul 2021

ProteinBERT: A universal deep-learning model of protein sequence and function
[version 1; not peer reviewed]

Dan Ofer¹, Nadav Brandes¹, Michal Linial¹, Nadav Rappoport, Yam Peleg

Author Affiliations

¹ Hebrew University of Jerusalem, Israel

Presented at

ISMB/ECCB

ISMB 2020

Representation Learning in Biology 2021

Abstract

Competing Interests

No competing interests were disclosed

Keywords

machine learning, Representation Learning, Biology, Proteins, Deep learning, BERT, Language model, Protein language model, NLP, ML, ProteinBERT, Neural networks, AI, statistics, protein function

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