A topological AUC-based biomarker ensemble method for the complex disease analysis
Bibliographic record
Abstract
Complex diseases are affected by many factors, and their pathogenic mechanism is complicated, which brings difficulties to the analysis and treatment of diseases. AUC, the area under the ROC curve, is often used as a gold standard to evaluate the performance of a binary classifier. The existing methods of constructing classifier by optimizing AUC are easy to fall into local optimum, and have high time complexity, which is not suitable for real-time analysis of high-dimensional gene expression data. With the rapid development of high-throughput sequencing technology, feature selection and model estimation become the necessary means to reduce the dimension and complexity of data, and the selected important features have the potential as biomarkers to reveal the pathogenesis of diseases. In this paper, we proposed a topological AUC-based biomarker ensemble method for the complex disease analysis, which uses gene expression data and the topological information derived from the protein-protein interaction network to identify biomarkers. The main contribution is to optimize two objectives simultaneously: maximizing the AUC score and minimizing the number of selected features. We applied the proposed method to analyze two types of problems: 1) prognosis of breast cancer, 2) classification of similar diseases. The results show that our method can effectively identify a small set of biomarkers with the powerful classification ability and the biological interpretability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".