source · discovered

scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning

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

Sources and history

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

No supporting source is linked yet.

Technical metadata and extraction receipts

Stable ID: scxdr-2026

areas: cells-tissues
url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12859067/
version: PMC archival version PMC12859067.1
retrieved at: 2026-09-15T23:29:32Z
doi: 10.1038/s42003-025-09418-5
publication status: peer_reviewed
year: 2026
artifact sha256: 47b5925e9887d87fc8288d29288802b1d67d54f064d913151df92171f7c68d33
artifact url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12859067/fullTextXML
artifact retrieved at: 2026-09-16T10:33:50.056Z
legacy paper: id: scxdr-2026; title: scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning; year: 2026; publication status: peer_reviewed; version: PMC archival version PMC12859067.1; source url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12859067/; primary domain: cells-tissues; retrieved utc: 2026-09-15T23:29:32Z; notes: Primary full text verified using Europe PMC XML; venue: Communications Biology; PMC ID: PMC12859067.; doi: 10.1038/s42003-025-09418-5
scope decision: included
missing metadata: licence: not_reported_in_legacy_extract

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