source · discovered

Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning

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: protein-binding-sites-2023

areas: proteins-complexes
url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9849350/
version: version of record
retrieved at: 2026-09-15T23:37:05Z
doi: 10.1038/s42003-023-04462-5
publication status: peer_reviewed
year: 2023
artifact sha256: 491711aa7186e74bf33f6d601c4ea6a8f565e770938fe1f91a0cd347b8f06f3f
artifact url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9849350/fullTextXML
artifact retrieved at: 2026-09-16T10:41:06Z
legacy paper: id: protein-binding-sites-2023; title: Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning; year: 2023; publication status: peer_reviewed; version: version of record; source url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9849350/; primary domain: proteins-complexes; retrieved utc: 2026-09-15T23:37:05Z; notes: Primary full text verified via Europe PMC fullTextXML; venue: Communications Biology; PMC ID: PMC9849350. Downstream binding-site classifier; not a native ProtT5 prediction head.; doi: 10.1038/s42003-023-04462-5
scope decision: included
missing metadata: licence: not_reported_in_legacy_extract

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