An Analytical Approach of Micro-RNA Interaction Study in Ovarian Cancer
Bibliographic record
Abstract
A gynecological malignancy with a high mortality rate and a variety of forms, ovarian cancer is challenging to identify early and prevent. Numerous studies have examined the expression profile of microRNAs (miRNAs) in tissue and serum samples of patients to discover pertinent biomarkers for ovarian cancer. Functional tests also demonstrated that a number of miRNAs had oncogenic or suppressive effects. Despite the invention of various biological markers, mostly mRNA and protein, ovarian cancer mortality remains a problem due to late detection, which is related to low specificities and sensitivities. Recent advancements in expression biology have turned towards this compulsive direction for cancer diagnosis and prognosis; focusing on identification and development of specific and sensitive biomarkers, such as microRNAs (miRNAs). A number of miRNAs, including miR-23b, miR- 146b, miR-200-a/b/c, miR-630, miR-31 influence the epithelial-mesenchymal transition pathway, modulating ovarian cancer cell invasiveness. MiRNAs have such a broad range of functions in ovarian cancer that they have been recommended as potential treatment strategies in the future. In this review, we discuss the role of various micro RNAs and the genes they target in the pathogenesis of ovarian cancer, as well as the potential applications of these molecules as biomarkers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".