Transcriptomic Classification of Genetically Engineered Mouse Models of Breast Carcinoma Identifies Human Subtype Counterparts
Background: Human breast cancer is a heterogeneous disease consisting of multiple molecular subtypes. Genetically engineered mouse models (GEMMs) are useful resources for studying breast cancers in vivo under genetically controlled and immune competent conditions. Identifying murine models with conserved human tumor features will facilitate etiology determinations, highlight the effects of mutations on pathway activation, and improve preclinical drug validation. Results: Transcriptomic profiles...
Provenance — who produced it, who reused it
Linked to 6 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- Transcriptomic classification of genetically engineered mouse mo... 2013 · 252 cites
- Treg depletion potentiates checkpoint inhibition in claudin-low... 2017 · 142 cites
- Network-based assessment of HDAC6 activity predicts preclinical... 2022 · 77 cites
3 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently