Gene expression profiling in ovarian cancer
The introduction of microarray techniques to cancer research brought great expectations for finding biomarkers that would improve patients’ treatment; however, the results of such studies are poorly reproducible and critical analyses of these methods are rare. In this study, we examined global gene expression in 97 ovarian cancer samples. Also, validation of results by quantitative RT-PCR was performed on 30 additional ovarian cancer samples. We carried out a number of systematic analyses in rel...
Provenance — who produced it, who reused it
Linked to 56 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.
- Small extracellular vesicles containing arginase-1 suppress T-ce... 2019 · 313 cites
- RNA demethylase ALKBH5 promotes ovarian carcinogenesis in a simu... 2020 · 112 cites
- Long noncoding RNA expression signature to predict platinum-base... 2017 · 89 cites
- Deep learning-based ovarian cancer subtypes identification using... 2020 · 88 cites
52 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
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently