<i>RadiationGeneSigDB</i> : A database of oxic and hypoxic radiation response gene signatures and their utility in identification of hypoxia-regulated MicroRNA
Bibliographic record
Abstract
Abstract Summary Radiation therapy is among the most effective and widely used modalities of cancer therapy in current clinical practice. With the advent of new high throughput genomic technologies and the continuous inflow of transcriptomic data, there has been a paradigm shift in the landscape of radiation oncology. In this era of personalized radiation medicine, genomic datasets hold great promise to investigate novel biomarkers predictive of radiation response. In this regard, the number of available gene expression based signatures built under oxic and hypoxic conditions is getting larger. This poses two main questions in the field, namely, i) how reliable are these signatures when applied across a compendium of datasets in different model systems; and ii) is there redundancy of gene signatures. To address these fundamental radiobiologic questions, we curated a database of gene expression signatures predictive of radiation response under oxic and hypoxic conditions. RadiationGeneSigDB has a collection of 11 oxic and 24 hypoxic signatures with the standardized gene list as a gene symbol, Entrez gene ID, and its function. We present the utility of this database through three case studies: i) comparing breast cancer oxic signatures in cell line data vs. patient data; ii) comparing the similarity of head and neck cancer hypoxia signatures in clinical tumor data; and iii) gaining an understanding of hypoxia-associated miRNA. This valuable, curated repertoire of published gene expression signatures provides a motivating example for how to search for similarities in radiation response for tumors arising from different tissues across model systems under oxic and hypoxic conditions, and how a well-curated set of gene signatures can be used to generate novel hypotheses about the functions of non-coding RNA. Availability and implementation RadiationGeneSigDB is implemented in R. The source code of this package and signatures can be downloaded from the GitHub: https://github.com/vmsatya/RadiationGeneSigDB
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".