Survival Related Profile, Pathways and Transcription Factors in Ovarian Cancer
To date, a variety of studies have employed gene expression profiling to classify ovarian carcinomas in clinically relevant subtypes. These studies provided valuable first clues to molecular changes in ovarian cancer that might be exploited in new treatment strategies. However, most studies were of relatively limited size and the number of overlapping genes in the identified profiles was minimal. Although identification of gene expression profiles associated with clinically relevant subtypes in...
Provenance — who produced it, who reused it
Linked to 26 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.
- curatedOvarianData: clinically annotated data for the ovarian ca... 2013 · 213 cites
- A network module-based method for identifying cancer prognostic... 2012 · 165 cites
- HOTAIR and its surrogate DNA methylation signature indicate carb... 2015 · 153 cites
- Angiogenic mRNA and microRNA Gene Expression Signature Predicts... 2012 · 150 cites
22 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
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.