Hierarchical Categorical Generative Modeling for Multi-omics Cancer Subtyping
Bibliographic record
Abstract
Identifying a specific cancer subtype from a variety of candidates is vital for precise and effective treatment. However, cancer subtyping is highly non-trivial as a result of cancer heterogeneity. While significant efforts have been put into understanding the mechanism of cancer subtypes via studying the omics data, existing methods run the risk of presenting biased analyses resulted from overfitting the high-dimensional and scarce omics data. In this paper, we propose a novel generative model that directly models the cancer data distribution by which downstream tasks can circumvent the curse of overfitting and achieve better performance. Unlike conventional generative modeling schemes, the proposed method underlines hierarchical categorical latent spaces to extract global features and local details respectively from transcriptomics and genomics profiles, which is the first to be considered in the cancer subtyping literature. By extensive experiments we verify that the proposed architecture achieves more clearly separated subtypes, as well as medically significant insights into real subtyping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".