Recent Advances in the Identification of Genetic and Biochemical Components of Breast Cancer Predisposition
Bibliographic record
Abstract
Breast cancer is one of the most common malignancies affecting women; thus, much effort has been put into understanding the genetics of predisposition to breast cancer as well as identifying factors involved in tumor progression and cancer prognosis. Conventional genetics was used to map and clone the two breast / ovarian cancer predisposition genes, BRCA1 and BRCA2. However, the vast majority of breast cancer cases are of a sporadic nature, likely due to a combination of environmental and genetic factors. Other genes have been identified via the analysis of protein interactions, screens based on inverse genomics, yeast two-hybrid assays, far Westerns, GST-pull downs and chromatography / mass spectrometry have been used to identify a number of proteins that interact with BRCA1 or BRCA2. Biological characteristics such as expression levels, protein stability and phosphorylation as well as the biological roles of the BRCA proteins in DNA repair and transcription have also led to the identification of o ther proteins involved in breast cancer. Recent advances in microarray analysis have allowed the identification of further genetic factors by comparing the transcription profiles of cell lines with varying levels of BRCA1 expression or drug resistance and tumors from patientswith or without BRCA1 / 2 mutations or with different pathobiological types of tumors or prognoses. Additionally, microarray analysis at the DNA level allows for the identification of genes that have been amplified or deleted during cancer progression, and tumor tissue arrays can be used to analyze hundreds of samples simultaneously for the expression of previously identified genetic factors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".