Gene array prediction of AML transformation in MDS
Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome (MDS) An array-based Diagnostic Classifier (DC) model, developed for and evaluated during the MILE study, correctly identified ~50% of the unfractionated MDS specimens submitted to the study; predictions for the other samples were split between “none-of-the-targets” classes and AML signatures, but this distinction also reflected clin...
Provenance — who produced it, who reused it
Linked to 15 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.
- Targeting the RNA m6A Reader YTHDF2 Selectively Compromises Canc... 2019 · 552 cites
- Applications of Bayesian network models in predicting types of h... 2018 · 87 cites
13 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