Mammary Cancer and Social Interactions: Identifying Multiple Environments That Regulate Gene Expression Throughout the Life Span
Bibliographic record
Abstract
Now that the human genome has been sequenced, along with those of major animal models, there is an urgent need to define those environments that interact with genes. The traditional view focuses on ways that gene products interact with the nuclear environment to regulate cell function, causing the physiologic changes, behaviors, and diseases manifest throughout development and aging. Although this view is essential, it is equally essential to understand the converse relationship, namely, to identify those environments at higher levels of organization that regulate the expression of specific genes. Given the vastness of this problem, one effective strategy is to start with a trait for which some of the genes have already been identified, such as malignant disease. In rats, social isolation and hypervigilance increase the incidence of mammary tumors, accelerate aging, and shorten the life span. We propose that similar environmental regulation of gene expression may underlie the disproportionately high mortality from premenopausal breast cancer of Blacks, a minority group that can experience high levels of loneliness and hypervigilance. Our goal is to identify which environments-social, psychological, hormonal, and cellular-regulate genetic mechanisms of mammary cancer risk as well as the specific times in the life span when they do so.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".