New Results
ProteinBERT: A universal deep-learning model of protein sequence and function
View ORCID ProfileNadav Brandes, View ORCID ProfileDan Ofer, Yam Peleg, View ORCID ProfileNadav Rappoport, View ORCID ProfileMichal Linial
doi: https://doi.org/10.1101/2021.05.24.445464
Nadav Brandes
1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
Dan Ofer
2Medtronic Inc
Yam Peleg
3Deep Trading ltd
Nadav Rappoport
4Department of Software and Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
Michal Linial
5Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel

Article usage
Posted May 25, 2021.
ProteinBERT: A universal deep-learning model of protein sequence and function
Nadav Brandes, Dan Ofer, Yam Peleg, Nadav Rappoport, Michal Linial
bioRxiv 2021.05.24.445464; doi: https://doi.org/10.1101/2021.05.24.445464
Subject Area
Subject Areas
- Biochemistry
- Biochemistry (14175)
- Bioengineering (10827)
- Bioinformatics (34316)
- Biophysics (17656)
- Cancer Biology (14758)
- Cell Biology (20784)
- Clinical Trials (138)
- Developmental Biology (11184)
- Ecology (16503)
- Epidemiology (2067)
- Evolutionary Biology (20813)
- Genetics (13677)
- Genomics (19100)
- Immunology (14246)
- Microbiology (33160)
- Molecular Biology (13835)
- Neuroscience (72411)
- Paleontology (542)
- Pathology (2278)
- Pharmacology and Toxicology (3860)
- Physiology (6102)
- Plant Biology (12391)
- Synthetic Biology (3461)
- Systems Biology (8371)
- Zoology (1913)