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 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".