IDENTIFICATION OF SHORTENED 3′ UNTRANSLATED REGIONS FROM EXPRESSION ARRAYS
Bibliographic record
Abstract
Cancer cells have been recently shown to express high level of short 3'UTR isoforms that can escape miRNA-mediated regulation. We present here a computational procedure for systematically identifying shortened 3'UTRs by Affymetrix 3' microarrays. The advantage of this technology compared to more recent and promising ones such as exon arrays and RNA-Seq is that, giving the relatively small cost, already existing datasets in public databases include a considerably higher number of experiments. Moreover, the design of Affymetrix Gene Chips is well-suited for 3'UTR analysis of a large number of genes. Initially, Affymetrix individual probes are regrouped into customized probesets mapping specifically the CDS or the 3'UTR of the transcript, according to RefSeq annotation. Then, candidate 3'UTR shortening events are identified by statistical differential expression analysis of customized probesets in different biological conditions. The procedure has been applied to expression data from two ovarian adenocarcinoma datasets. Selected gene sets are significantly enriched for annotated splice variant genes as well as genes involved in estrogen dependent cancer mechanisms, confirming the validity of the proposed procedure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".