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Leveraging conformal prediction to annotate enzyme function space with limited false positives

PLoS Computational Biology(2024)10.1371/journal.pcbi.1012135Source: DataRank Database

Leveraging conformal prediction to annotate enzyme function space with limited false positives is a research paper published in PLoS Computational Biology (2024). On theSindex it has a DataRank of 0. It has been cited 3 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access3 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (10 grants)Impact (FWCI 0.76)OA: goldTopics (38)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 (3)

Jiaqi Luo ORCID,Yunan Luo ORCID,Kerr Ding ORCID

Live enrichment

PubMed:38809942 OpenAlex:https://openalex.org/W4399140945

National Institute of General Medical Sciences

Grant: R35GM150890

Amazon

Grant: AWS AI CFP 2022

University of Illinois at Urbana-Champaign

Grant: MMLI Seed Grant (grant #2019897)

National Science Foundation

Grant: 2138307

Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT)

National Science Foundation

Grant: 2138259

Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS)

National Institutes of Health

Grant: 1R35GM150890-01

Integrative deep learning algorithms for understanding protein sequence-structure-function relationships: representation, prediction, and discovery

National Science Foundation

Grant: 2138286

Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI

National Science Foundation

Grant: 2019897

Molecule Maker Lab Institute (MMLI): An AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing

National Science Foundation

Grant: 2137603

Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services

National Science Foundation

Grant: 2138296

ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI)

FWCI

0.76

Citation Percentile

0.7%

Citation Trend

2024

2025

goldLicense: cc-by

journalhttps://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012135&type=printable

publisherhttps://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012135&type=printable

publisherhttps://dx.plos.org/10.1371/journal.pcbi.1012135

journalhttps://doi.org/10.1371/journal.pcbi.1012135

repositoryhttps://pubmed.ncbi.nlm.nih.gov/38809942

Fields of Study

Computational Drug Discovery MethodsProtein Structure and DynamicsMachine Learning in Materials Science0301 basic medicine0303 health sciences03 medical and health sciencesEnzymesComputational BiologyMachine LearningFalse Positive ReactionsDeep LearningHumans

MeSH Terms

Machine LearningDeep LearningEnzymesFalse Positive ReactionsHumansComputational Biology

Keywords

False discovery rateComputer scienceFalse positive paradoxDrug discoveryData miningMachine learningFunction (biology)BiomedicineSet (abstract data type)True positive rateArtificial intelligenceFalse positives and false negativesBioinformaticsBiologyDeep LearningComputational BiologyHumansFalse Positive ReactionsResearch ArticleEnzymes

Sustainable Development Goals

SDG 0: Partnerships for the goals