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Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography

Bioinformatics(2021)10.1093/bioinformatics/btab123Source: DataRank Database

Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography is a research paper published in Bioinformatics (2021). On theSindex it has a DataRank of 0. It has been cited 12 times.

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Open Access12 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (15 grants)Impact (FWCI 1.41)OA: greenTopics (38)IDs (PubMed, PMC, OpenAlex)SDGs (1)

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 (8)

Haohan Wang ORCID,Zhenxi Zhu,Xiangrui Zeng ORCID,Yi‐Wei Chang ORCID,Jing Zhang ORCID

Live enrichment

PubMed:33620460 PMC:PMC12158443 OpenAlex:https://openalex.org/W3131203871

U.S. National Institutes of Health

Grant: P41GM103712

U.S. National Institutes of Health

Grant: R01GM134020

U.S. National Institutes of Health

Grant: K01MH123896

U.S. National Science Foundation

Grant: DBI-1949629

U.S. National Science Foundation

Grant: IIS-2007595

Mark Foundation For Cancer Research

Grant: 19-044-ASP

Mark Foundation For Cancer Research

Grant: AMD COVID-19 HPC

National Science Foundation

Grant: 2007595

III: Small: Improving automation and speed of macromolecule recognition and localization in cryo-electron tomography using unsupervised deep learning

National Science Foundation

Grant: 1949629

IIBR Informatics: Reducing the training data annotation cost for learning-based macromolecule identification in cellular electron cryo-tomography

National Institutes of Health

Grant: 1R01GM134020-01A1

Novel machine learning approaches for improving structural discrimination in cryo-electron tomography

National Institutes of Health

Grant: 5P41GM103712-08

High Performance Computing for Multiscale Modeling of Biological Systems

National Institutes of Health

Grant: 5P41GM103712-02

High Performance Computing for Multiscale Modeling of Biological Systems

National Institutes of Health

Grant: 5K01MH123896-02

A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders

Center for Machine Learning and Health

Carnegie Mellon University

FWCI

1.41

Citation Percentile

0.8%

Citation Trend

2021

2022

2025

2026

greenLicense: OUP Standard Publication Reuse

repositoryhttps://arxiv.org/pdf/2102.12040

publisherhttp://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab123/36599597/btab123.pdf

publisherhttps://academic.oup.com/bioinformatics/article-pdf/37/16/2340/59818313/btab123.pdf

repositoryhttp://arxiv.org/abs/2102.12040

Fields of Study

Advanced Electron Microscopy Techniques and ApplicationsMachine Learning in BioinformaticsComputational Physics and Python Applications02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

Keywords

In situCryo-electron tomographyTomographyMacromoleculeElectron tomographyComputer scienceComputed tomographyArtificial intelligenceElectronBiophysicsChemistryPhysicsBiologyMedicineOpticsRadiologyElectron microscopeBiochemistryNuclear physicsFOS: Computer and information sciencesComputer Science - Machine LearningBioinformaticsComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionMathematical sciencesBiological SciencesQuantitative Biology - Quantitative MethodsMachine Learning (cs.LG)Machine LearningInformation and Computing SciencesFOS: Biological sciencesGeneric health relevanceQuantitative Methods (q-bio.QM)

Sustainable Development Goals

SDG 0: Reduced inequalities