Computational Identification of RNA-Seq Based miRNA-Mediated Prognostic Modules in Cancer
Bibliographic record
Abstract
Systematic identification of miRNA prognostic signature can help decipher the effects of biomarkers in cancer treatment. A number of previous studies have only characterized a single miRNA as a promising prognostic biomarker. There is currently a trend toward combining several miRNAs as a panel of prognostic signatures, but few attempts to explain the mechanism of miRNA combination. Throughout this paper, we refer to "miRNA-mediated prognostic modules" and propose a novel computational approach called ProModule to analyze prognostic biomarkers from the module perspective. ProModule works in two main stages: it first uses univariate and multivariable Cox proportional hazard regressions to find individual miRNA biomarkers and then employs a clustering method to systematically detect miRNA-mediated modules with statistical prognostic significance. We applied ProModule to three data sets in bladder cancer, breast cancer, and liver cancer, and identified several miRNA prognostic modules for each data set. We found that miRNA prognostic modules have more powerful prognostic value than individuals while presenting coherent miRNA-miRNA expression as well as significant functional enrichment, and thus are likely to be biologically meaningful. Availability: ProModule is implemented in R and available at https://github.com/chupan1218/ProModule.
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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 | 0.001 | 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".