piRSNP: A Database of piRNA- related SNPs and their Effects on CancerrelatedpiRNA Functions
Bibliographic record
Abstract
Backgroud: PIWI-interacting RNAs (piRNAs) are a kind of small non-coding RNAs which interact with PIWI proteins and play a vital role in safeguarding genome. Single nucleotide polymorphisms (SNPs) are widely distributed variations which are associated with diseases and have rich information. Up to now, various studies have proved that SNPs on piRNA were related to diseases. Objective: In order to create a comprehensive source about piRNA-related SNPs, we developed a publicly available online database piRSNP. Methods: We systematically identified SNPs on human and mouse piRNAs. piRSNP contains 42,967,522 SNPs on 10,773,081 human piRNAs and 29,262,185 SNPs on 16,957,706 mouse piRNAs. Results: 7,446 SNPs on 519 cancer-related piRNAs and their flanks are investigated. Impacts of 2,512 variations of cancer-related piRNAs on piRNA-mRNA interactions are analyzed. Conclusion: All these useful data and piRNA expression profiles of 12 cancer types in both tumor and pericarcinomatous tissues are compiled into piRSNP. piRSNP characterizes human and mouse piRNArelated SNPs comprehensively and could be beneficial for researchers to investigate subsequent piRNA functions. Database URL is http://www.ibiomedical.net/piRSNP/.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".