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Detecting anomalies from liquid transfer videos in automated laboratory setting

Frontiers in Molecular Biosciences(2023)10.3389/fmolb.2023.1147514Source: DataRank Database

Detecting anomalies from liquid transfer videos in automated laboratory setting is a research paper published in Frontiers in Molecular Biosciences (2023). On theSindex it has a DataRank of 0. It has been cited 6 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access6 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (12 grants)Impact (FWCI 0.92)OA: goldTopics (24)IDs (PubMed, 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)

Zaber Abdul Hakim,Ali Dabouei ORCID,Mostofa Rafid Uddin,Zachary Freyberg ORCID,Andy MacWilliams

Live enrichment

PubMed:37214339 OpenAlex:https://openalex.org/W4377564914

National Institutes of Health

Grant: R01GM134020 P41GM103712

National Science Foundation

Grant: DBI-1949629 IIS-2007595 MCB-2205148

Mark Foundation For Cancer Research

Grant: 19-044-ASP

Advanced Micro Devices

Grant: COVID-19 HPC

National Science Foundation

Grant: 2205148

Tools4Cells: Machine-learning aided morphodynamics characterization of stem cell differentiation using label-free microscopies

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 Institutes of Health

Grant: 5P41GM103712-08

High Performance Computing for Multiscale Modeling of Biological Systems

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 Science Foundation

Grant: 2211597

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

NIGMS NIH HHS

Grant: P41 GM103712

NIGMS NIH HHS

Grant: R01 GM134020

FWCI

0.92

Citation Percentile

0.8%

Citation Trend

2024

2025

2026

goldLicense: cc-by

journalhttps://www.frontiersin.org/articles/10.3389/fmolb.2023.1147514/pdf

publisherhttps://www.frontiersin.org/articles/10.3389/fmolb.2023.1147514/pdf

publisherhttps://www.frontiersin.org/articles/10.3389/fmolb.2023.1147514/full

journalhttps://doi.org/10.3389/fmolb.2023.1147514

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37214339

Fields of Study

Anomaly Detection Techniques and ApplicationsWater Systems and OptimizationData Stream Mining Techniques0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Computer scienceAnomaly detectionTransfer of learningArtificial intelligenceMargin (machine learning)Feature (linguistics)Software deploymentPattern recognition (psychology)Set (abstract data type)AutomationFeature extractionMachine learningData miningaction recognitionQH301-705.5lab automationvideo anomaly detectionMolecular BiosciencesBiology (General)

Sustainable Development Goals

SDG 0: Industry, innovation and infrastructure