CRC samples for FOLFOX therapy prediction
The aim of this study is to identify responders to FOLFOX therapy by applying the Random Forests (RF) algorithm to gene expression data. Eighty-three unresectable colorectal cancer (CRC) patients including 42 responders and 41 non-responders were divided into training (54 patients) and test (29 patients) sets.
Provenance — who produced it, who reused it
Linked to 17 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.
- Machine learning-based integration develops an immune-derived ln... 2022 · 914 cites
- Integrative analysis from multi-center studies identities a cons... 2021 · 213 cites
- DDX39B drives colorectal cancer progression by promoting the sta... 2022 · 129 cites
- An integrative analysis reveals functional targets of GATA6 tran... 2013 · 81 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