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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
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data for protein model refinement and model quality assessment

Authors: Xu, Jinbo;

data for protein model refinement and model quality assessment

Abstract

This dataset contains protein 3D models that can be used to train a machine learning or deep learning method for protein model refinement and model quality assessment. Meanwhile, Data4GNNRefine-part1.tar.gz includes the protein 3D models built by RaptorX for more than 25000 CathS35 proteins. Both the template-free and template-based methods are used to build these 3D models. Data4GNNRefine-part2.tar.gz includes the protein models released by CASPs and CAMEO. This file also contains the native structures of the protein models.

{"references": ["https://www.biorxiv.org/content/10.1101/2020.12.10.419994v1"]}

Related Organizations
Keywords

protein structure prediction, machine learning, deep learning, protein model quality assessment, protein model refinement

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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