Avoiding test set bias with rank-based prediction
Bibliographic record
Abstract
Abstract Background Prior to applying genomic predictors to clinical samples, the genomic data must be properly normalized. The most effective normalization methods depend on the data from multiple patients. From a biomedical perspective this implies that predictions for a single patient may change depending on which other patient samples they are normalized with. This test set bias will occur when any cross-sample normalization is used before clinical prediction. Methods We developed a new prediction modeling framework based on the relative ranks of features within a sample in order to prevent the need for cross-sample normalization, therefore effectively avoiding test set bias. We employed modeling with previously published Top-Scoring Pairs (TSPs) methodology to build the rank-based predictors. We further investigated the robustness of the rank-based models in case of heterogeneous datasets using diverse microarray technologies. Results We demonstrated that results from existing genetic signatures which rely on normalizing test data may be unreproducible when the patient population changes composition or size. Using pairwise comparisons of features, we produced a ten gene, platform-robust, and interpretable alternative to the PAM50 subtyping signature and evaluated the robustness of our signature across 6,297 patients samples from 28 curated breast cancer microarray datasets spanning 15 different platforms. Conclusion We propose a new approach to developing genomic signatures that avoids test set bias through the robustness of rank-based features. Our small, interpretable alternative to PAM50 produces comparable predictions and patient survival differentiation to the original signature. Additionally, we are able to ensure that the same patient will be classified the same way in every context.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".