The involvement of microRNAs in malignant transformation.
Bibliographic record
Abstract
In the multiple steps in cancer progression, microRNAs (miRNAs) play significant roles in each stage. Reports of considerable differences in expression levels of miRNAs between normal and malignant tissues are understandable considering miRNAs are key regulators of gene expression. Dysregulation of miRNA expression levels in neoplasia occurs because many miRNAs are located in "fragile sites", which are frequently deleted in cancer. miRNAs are often down regulated in cancerous tissues and target oncogenic proteins are classified as tumour suppressor miRNAs, such as let-7. While, miRNAs that are frequently over-expression in neoplastic tissues compared to normal tissues and regulate tumour suppressor proteins are categorized as "oncomiRs". In this review, we summarize information about microRNAs involved in the emerging field of cancer stem cells, and microRNAs involved in breast cancer, an area of our expertise. The application of miRNAs to cancer therapeutics and diagnostics is emerging as an important field of gene therapy. The diverse nature of miRNAs in cancer is continually being elucidated to lead to the enigmatic treatment options for neoplastic disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".