Bibliographic record
Abstract
microRNAs (miRNAs) are a recently discovered class of small non-coding RNAs that regulate gene expression. Rapidly accumulating evidence has revealed that miRNAs are associated with cancer. The human tissue kallikrein gene family is the largest contiguous family of proteases in the human genome, containing 15 genes. Many kallikreins have been reported as potential tumor markers. In this review, recent bioinformatics and experimental evidence is presented indicating that kallikreins are potential miRNA targets. The available experimental approaches to investigate these interactions and the potential diagnostic and therapeutic applications are also discussed. miRNAs represent a possible regulatory mechanism for controlling kallikrein expression at the post-transcriptional level. Many miRNAs were predicted to target kallikreins and a single miRNA can target more than one kallikrein. Recent evidence suggests that miRNAs can also exert 'quantitative' control of kallikreins by utilizing multiple targeting sites in the kallikrein mRNA. More research is needed to experimentally verify the in silico predictions and to investigate the possible role in tumor initiation and/or progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".