Non-coding RNAs including miRNAs, piRNAs, and tRNAs in human cancer
Bibliographic record
Abstract
// Mieke Heyns 1 and Olga Kovalchuk 1 1 Department of Biological Sciences, University of Lethbridge, Lethbridge, Canada Correspondence to: Olga Kovalchuk, email: // Keywords : cancer, non-coding RNA, miRNA, piRNA, tRNA Received : July 01, 2015 Accepted : July 17, 2015 Published : July 31, 2015 Abstract Over 98% of our genes code for RNA transcripts that will never become translated into protein. Numerous non-coding RNA (ncRNA) transcripts are structurally and functionally diverse. In particular, micro RNAs (miRNAs), piwi-interacting RNAs (piRNAs), and, more recently, transfer RNAs (tRNAs) are implicated as regulators of key genes and processes that are involved in various human diseases, including cancer. Here, we summarize the recent findings and perspectives in the small RNA and cancer research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".