Dependence model and network for biomarker identification and cancer classification
Bibliographic record
Abstract
Of particular interest in this paper is to develop statistical and modeling approaches for protein biomarker discovery to provide new insights into the early detection and diagnosis of cancer, based on mass spectrometry (MS) data. In this paper, we propose to employ an ensemble dependence model (EDM)-based framework for cancer classification, protein dependence network reconstruction, and further for biomarker identification. The dependency revealed by the EDM reflects the functional relationships between MS peaks and thus provides some insights into the underlying cancer development mechanism. The EDM-based classification scheme is applied to real cancer MS datasets, and provides superior performance for cancer classification when compared with the popular Support Vector Machine algorithm. From the eigenvalue pattern of the dependence model, the dependence networks are constructed to identify cancer biomarkers. Furthermore, for the purpose of comparison, a classification-performance-based biomarker identification criterion is examined. The dependence-network-based biomarkers show much greater consistency in cross validation. Therefore, the proposed dependence-network-based scheme is promising for use as a cancer diagnostic classifier and predictor. 1.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".