Editorial: Exosomes, miRNAs, and lncRNAs in breast cancer: Therapeutic and diagnostic applications
Bibliographic record
Abstract
Breast cancer is a complex disease that can be influenced by a variety of factors, including genetics, lifestyle, and environmental factors. It is classified into different subtypes based on the presence or absence of hormone receptors and HER2 expression, as well as the signature profile of the gene of the tumor cells. The presence of estrogen and progesterone receptors, which are proteins that respond to female hormones, is a paramount factor not only in breast cancer development but also treatment.Tumors that express these receptors are referred to as ER-positive or PRpositive, and can often be treated with hormone therapy. HER2 is another protein that plays a role in the growth and division of cells, and tumors that overexpress HER2 are referred to as HER2-positive. In addition to these receptor-based subtypes, breast cancer can also be classified based on gene expression profiles, which can provide insight into the underlying biology of the tumor and help guide treatment decisions. Some common gene expression subtypes include HER2-enriched, luminal A, and B type, and triple-negative (when ER, PR, and HER2 are absent) breast cancer.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".