source · discovered

AUPRC: a metric for evaluating the performance of in-silico perturbation methods in identifying differentially expressed genes

Primary paper retained with its original identifier. Metadata inherited from the literature collection; individual result checks are separate.

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Release 2026-09-16-d74d282221a9 · Record review: discovered

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Technical metadata and extraction receipts

Stable ID: insilico-perturbation-auprc-2025

areas: cells-tissues
url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12400816/
version: PMC archival version PMC12400816.1
retrieved at: 2026-09-15T23:37:05Z
doi: 10.1093/bib/bbaf426
publication status: peer_reviewed
year: 2025
artifact sha256: 2715709d94f84744afa32cafdcaa72efd206d63af8c60afe7619b2cb90108b6b
artifact url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12400816/fullTextXML
artifact retrieved at: 2026-09-16T10:41:06Z
legacy paper: id: insilico-perturbation-auprc-2025; title: AUPRC: a metric for evaluating the performance of in-silico perturbation methods in identifying differentially expressed genes; year: 2025; publication status: peer_reviewed; version: PMC archival version PMC12400816.1; source url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12400816/; primary domain: cells-tissues; retrieved utc: 2026-09-15T23:37:05Z; notes: Primary full text verified via Europe PMC fullTextXML; venue: Briefings in Bioinformatics; PMC ID: PMC12400816. Paper benchmarks metrics and scGen perturbation method; no foundation-model result in this row.; doi: 10.1093/bib/bbaf426
scope decision: included
missing metadata: licence: not_reported_in_legacy_extract

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