Self-supervised Protein Representation Learning
Self-Supervised Protein Representation Learning is a computational framework that belongs to the class of unsupervised learning methods, specifically distinguished by its ability to extract meaningful amino acid sequence representations through pretext tasks designed on unlabeled protein data without external annotation.
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2017–2026
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ProteinBERT: a universal deep-learning model of protein sequence and function
Nadav Brandes, Dan Ofer, Yam Peleg et al. · Bioinformatics · 2022 · 975 citations · Full text
Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2019 · 689 citations · Full text
UDSMProt: universal deep sequence models for protein classification
Nils Strodthoff, Patrick Wagner, Markus Wenzel et al. · Bioinformatics · 2020 · 211 citations · Full text
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Google (United States)
Mountain View, United States
4 h-index
4 publications
3.4K citations
Baidu (China)
Beijing, China
3 h-index
18 publications
392 citations
Fudan University
Shanghai, China
3 h-index
15 publications
550 citations
Berkeley College
New York, United States
2 h-index
14 publications
6K citations

Oak Ridge National Laboratory
Oak Ridge, United States
2 h-index
2 publications
2.6K citations

Shanghai Innovative Research Center of Traditional Chinese Medicine
Shanghai, China
2 h-index
2 publications
106 citations
Nvidia (United States)
Santa Clara, United States
2 h-index
6 publications
7.9K citations
University of Chicago
Chicago, United States
2 h-index
2 publications
114 citations
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Repository · 8 publications · 1.3K citations
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6 publications · 1.3K citations
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3 publications · 99 citations
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Repository · 3 publications · 81 citations
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PS · 2 publications · 46 citations
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