source · discovered

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis

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: scelmo-2025

areas: cells-tissues
url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12393277/
version: preprint archived 2025-08-23
retrieved at: 2026-09-15T23:25:00Z
doi: 10.1101/2023.12.07.569910
publication status: preprint
year: 2025
artifact sha256: ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316
artifact url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12393277/fullTextXML
artifact retrieved at: 2026-09-16T10:41:16.537541+00:00
legacy paper: id: scelmo-2025; title: scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis; year: 2025; publication status: preprint; version: preprint archived 2025-08-23; source url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12393277/; primary domain: cells-tissues; retrieved utc: 2026-09-15T23:25:00Z; notes: Primary full text via Europe PMC XML; venue: bioRxiv; PMC ID: PMC12393277.; doi: 10.1101/2023.12.07.569910
scope decision: included
missing metadata: licence: not_reported_in_legacy_extract

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