Bibliographic record
Abstract
MicroRNA (miRNA) is an endogenous non-protein coding small RNA molecule that negatively regulates gene expression by the degradation of messenger RNA (mRNA) or the suppression of mRNA translation. miRNA plays important roles in physiologic processes such as cellular development, differentiation, proliferation, apoptosis, and stem cell self-renewal. Studies show that deregulation of miRNA expression is closely associated with tumorigenicity, invasion, and metastasis. The functionality of aberrant miRNAs in cancer could act either as oncogenes or tumor suppressors during tumor initiation and progression. Similar to protein-coding gene regulation, dysregulation of miRNAs may be related to changes in miRNA gene copy numbers, epigenetic modulation, polymorphisms, or biogenesis modifications. Elucidation of the miRNA expression profiles (miRNomes) of many types of cancers is starting to decode the regulatory network of miRNA-mRNA interactions from a systems biology perspective. Experimental evidence demonstrates that modulation of specific miRNA alterations in cancer cells using miRNA replacement or anti-miRNA technologies can restore miRNA activities and repair gene regulatory networks affecting apoptotic signaling pathways or drug sensitivity, and improve the outcome of treatment. Numerous animal studies for miRNA-based therapy offer the hope of targeting miRNAs as an alternative cancer treatment. Developing the small molecules to interfere with miRNAs could be of great pharmaceutical interest in the future.
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 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".