Gene expression microarray analysis and genome databases facilitate the characterization of a chromosome 22 derived homogenously staining region
Bibliographic record
Abstract
Karyotype and fluorescence in situ hybridization (FISH) analyses previously identified a homogeneously staining region (HSR) derived from chromosome 22 in OV90, an epithelial ovarian cancer (EOC) cell line. Affymetrix expression microarrays in combination with the UniGene and Human Genome Browser databases were used to identify the candidate genes comprising the amplicon of the HSR, based on comparison of expression profiles of OV90, EOC cell lines lacking HSRs and primary cultures of normal ovarian surface epithelial (NOSE) cells. A group of probe sets displaying a minimum 3-fold overexpression with a high reliability score (P-call) in OV90 were identified which represented genes that mapped within a 1-2 Mb interval on chromosome 22. A large number of probe sets, some of which represent the same genes, displayed no evidence of overexpression and/or low reliability scores (A-call). An investigation of the probe set sequences with the Affymetrix and Sanger Institute Chromosome 22 Group databases revealed that some of the probe sets displaying discordant results for the same gene were complementary to intronic sequences and/or the antisense strand. Microarray results were validated by RT-PCR. Genomic analysis suggests that the HSR was derived from the amplification of a 1.1 Mb interval defined by the chromosomal map positions of ZNF74 and Hs.372662, at 22q11.21. The deduced amplicon is derived from a complex region of chromosome 22 that harbors low-copy repeats (LCRs). The amplicon contains 18 genes as likely targets for gene amplification. This study illustrates that large-scale expression microarray analysis in combination with genome databases is sufficient for deducing target genes associated with amplicons and stresses the importance of investigating probe set design before engaging in validation studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".