Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets-Reference-Cited by-同舟云学术

Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets

Published:2021-05 Issue:5 Volume:1 Page:
ISSN:2691-1299
Container-title:Current Protocols
language:en
Short-container-title:Current Protocols

Author:

Dallago Christian¹²,Schütze Konstantin¹,Heinzinger Michael¹²,Olenyi Tobias¹,Littmann Maria¹²,Lu Amy X.³,Yang Kevin K.⁴,Min Seonwoo⁵,Yoon Sungroh⁵⁶,Morton James T.⁷,Rost Burkhard¹⁸⁹¹⁰¹¹

Affiliation:

1. TUM (Technical University of Munich) Department of Informatics Bioinformatics & Computational Biology Garching/Munich Germany

2. TUM Graduate School Center of Doctoral Studies in Informatics and its Applications (CeDoSIA) Garching/Munich Germany

3. Department of Computer Science University of Toronto Toronto Canada & Vector Institute

4. Microsoft Research New England Cambridge Massachusetts

5. Department of Electrical and Computer Engineering Seoul National University Seoul South Korea

6. Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea

7. Center for Computational Biology Flatiron Institute New York New York

8. Institute for Advanced Study (TUM‐IAS) Garching/Munich Germany

9. TUM School of Life Sciences Weihenstephan (WZW) Freising Germany

10. Columbia University Department of Biochemistry and Molecular Biophysics New York New York

11. New York Consortium on Membrane Protein Structure (NYCOMPS) New York New York

Funder

Deutsche Forschungsgemeinschaft

Publisher

Wiley

Subject

Medical Laboratory Technology,Health Informatics,General Pharmacology, Toxicology and Pharmaceutics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Neuroscience

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpz1.113

Reference65 articles.

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5. Language modelling for biological sequences—curated datasets and baselines;Armenteros J. J. A.;BioRxiv,2020

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