Integrin-mediated signal transduction in transgenic mouse models of human breast cancer
Bibliographic record
Abstract
The remarkable generation of scores of increasingly sophisticated mouse models of mammary cancer over the past two decades has provided tremendous insights into molecular derangements that can lead to cancer.The relationships of these models to human breast cancer, however, remain problematic.Recent advances in genomic technologies offer significant opportunities to identify critical changes that occur during cancer evolution and to distinguish in a complex and comprehensive manner the key similarities and differences between mouse models and human cancer.Comparisons between mouse and human tumors are being performed using comparative genomic hybridization, gene expression profiling, and proteomic analyses.The appropriate use of genetically engineered mouse models of mammary cancer in preclinical studies remains an important challenge which may also be aided by genomic technologies.Genomic approaches to cancer are generating huge datasets that represent a complex system of underlying networks of genetic interactions.Mouse models offer a tremendous opportunity to identify such networks and how they relate to human cancer.The challenge of the future remains to decipher these networks in order to identify the genetic nodes of oncogenesis that may be important targets for chemoprevention and therapy.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".