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Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling

Bioinformatics(2023)10.1093/bioinformatics/btad494Source: DataRank Database

Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling is a research paper published in Bioinformatics (2023). On theSindex it has a DataRank of 0. It has been cited 18 times.

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Open Access18 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (16 grants)Impact (FWCI 1.69)OA: goldTopics (21)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Genki Terashi ORCID,Daisuke Kihara ORCID,Sai Raghavendra Maddhuri Venkata Subramaniya ORCID

Live enrichment

PubMed:37549063 OpenAlex:https://openalex.org/W4385647426

National Institutes of Health

Grant: R01GM123055

National Institutes of Health

Grant: R01GM133840

National Institutes of Health

Grant: 3R01GM133840-02S1

Building protein structure models for intermediate resolution cryo-electron microscopy maps

National Science Foundation

Grant: IIS2211598

National Science Foundation

Grant: DMS2151678

National Science Foundation

Grant: CMMI1825941

National Science Foundation

Grant: MCB1925643

National Science Foundation

Grant: DBI2146026

National Science Foundation

Grant: DBI2003635

National Science Foundation

Grant: 2146026

Collaborative Research: Identification and Structural Modeling of Intrinsically Disordered Protein-Protein and Protein-Nucleic Acids Interactions

National Science Foundation

Grant: 1825941

Nanomanufacturing of Protein Macromolecular Frameworks Through an Integrated Bioengineering and Computational Approach

National Science Foundation

Grant: 2003635

IIBR Informatics: Development of Multimodal approaches for protein function prediction

National Science Foundation

Grant: 2211598

Collaborative Research: III: Medium: Systematic De Novo Identification of Macromolecular Complexes in Cryo-Electron Tomography Images

National Science Foundation

Grant: 1925643

Collaborative Research: RoL: Revealing a new mechanism of action for eukaryotic transcriptional activation domains

National Institutes of Health

Grant: 5R01GM123055-02

Structural Modeling of Multifarious Protein Complexes

National Science Foundation

Grant: 2151678

Collaborative Research: Integrated Moment-Based Descriptors and Deep Neural Network for Screening Three-Dimensional Biological Data

FWCI

1.69

Citation Percentile

0.8%

Citation Trend

2024

2025

2026

goldLicense: cc-by

journalhttps://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad494/51048006/btad494.pdf

publisherhttps://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad494/51048006/btad494.pdf

publisherhttps://academic.oup.com/bioinformatics/article-pdf/39/8/btad494/51225792/btad494.pdf

journalhttps://doi.org/10.1093/bioinformatics/btad494

journalhttps://dialnet.unirioja.es/servlet/articulo?codigo=3734447

Fields of Study

Genomics and Phylogenetic StudiesAdvanced Electron Microscopy Techniques and ApplicationsEnzyme Structure and Function0206 medical engineering02 engineering and technology

MeSH Terms

Models, MolecularProtein ConformationProteinsCryoelectron Microscopy

Keywords

Computer scienceGenerative grammarCryo-electron microscopyArtificial intelligenceDeep learningBiologyBiophysicsModels, MolecularOriginal PaperProtein ConformationCryoelectron MicroscopyProteins