RNA-Seq reveals an unprecedented complexity of the neuroblastoma transcriptome and is suitable for clinical endpoint prediction [RNA-Seq]
We generated gene expression profiles from 498 primary neuroblastomas using RNA-Seq and microarrays. We sought to systematically evaluate the capability of RNA deep-sequencing (RNA-Seq)-based classification for clinical endpoint prediction in comparison to microarray-based ones. The neuroblastoma cohort was randomly divided into training and validation sets, and 360 predictive models on six clinical endpoints were generated and evaluated. While prediction performances did not differ considerably...
Provenance — who produced it, who reused it
Linked to 22 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.
- Comparison of RNA-seq and microarray-based models for clinical e... 2015 · 429 cites
- Deep Learning-Based Multi-Omics Data Integration Reveals Two Pro... 2018 · 228 cites
- MYCN mediates cysteine addiction and sensitizes neuroblastoma to... 2022 · 224 cites
- On the utility of RNA sample pooling to optimize cost and statis... 2020 · 126 cites
- Transcriptome 3′end organization by PCF11 links alternative poly... 2018 · 106 cites
- PRMT5 activates AKT via methylation to promote tumor metastasis 2022 · 101 cites
- Integrating gene regulatory pathways into differential network a... 2019 · 81 cites
15 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