BCtypeFinder: A Semi-Supervised Model with Domain Adaptation for Breast Cancer Subtyping Using DNA Methylation Profiles
Bibliographic record
Abstract
Accurate breast cancer subtype prediction is critical for precise diagnosis, treatment planning, and prognosis evaluation. Recent studies highlight the important role of epigenetic modifications in breast tumor, especially the potential of abnormal DNA methylation patterns as markers for distinct subtypes. However, developing a reliable model for subtype prediction based on DNA methylation profiles is challenging due to the scarcity of annotated dataset. This work proposes BCtypeFinder, a breast cancer subtype prediction framework that utilizes a domain adaptation network combined with semi-supervised learning to address batch effects. Our model leverages both labeled and unlabeled DNA methylation data to extract domain-invariant features while aligning subtype distributions across various datasets. BCtypeFinder outperforms current methods, showcasing superior classification performance across multiple test cases. Furthermore, we explored the effects of batch correction in BCtypeFinder, demonstrating its ability to remove batch-specific variations among patients of the same subtype, thus improving the robustness of the classifier. BCtypeFinder is publicly available at https://github.com/joungmin-choi/BCtypeFinder.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".