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Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes

Research article (Proteins Structure Function and Bioinformatics, 2020) · cited 58× · AI/ML
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Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes

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Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes is a scholarly article[1].

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  • Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes's instance of is recorded as scholarly article[2].

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Ron Ofer Dror

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  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes. Retrieved May 24, 2026, from https://4ort.xyz/entity/hierarchical-rotationequivariant-neural-networks-to-select-structural-models-of-protein-complexes
MLA “Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/hierarchical-rotationequivariant-neural-networks-to-select-structural-models-of-protein-complexes.
BibTeX @misc{4ortxyz_hierarchical-rotationequivariant-neural-networks-to-select-structural-models-of-protein-complexes_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes}}, year = {2026}, url = {https://4ort.xyz/entity/hierarchical-rotationequivariant-neural-networks-to-select-structural-models-of-protein-complexes}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes — https://4ort.xyz/entity/hierarchical-rotationequivariant-neural-networks-to-select-structural-models-of-protein-complexes (retrieved 2026-05-24)

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