Mutual-assistance learning for trustworthy biomarker discovery and disease prediction
Bibliographic record
Abstract
Integrating and analyzing multiple omics datasets, such as genomics, environmental influences, and imaging endophenotypes, has yielded an abundance of candidate biomarkers. However, translating such findings into beneficial clinical knowledge for disease prediction remains challenging. This becomes even more challenging when studying interpretable high-order feature interactions such as gene-environment interaction (G$\times $E) to understand the etiology. To fill this gap, we draw on the idea of mutual-assistance (MA) learning and accordingly propose a fresh and powerful scheme, referred to as mutual-assistance causal biomarker discovery and stable disease prediction approach (MA-CBxDP). Specifically, we design an interpretable bi-directional mapping framework, integrated with a causal feature interaction module, to extract co-expression patterns across different modalities and identify trustworthy biomarkers including G$\times $E. A cooperative prediction module is further incorporated to ensure accurate diagnosis and identification of causal effects for pathogenesis. Importantly, biomarker discovery and disease prediction can mutually reinforce each other, helping to provide novel insights into chronic diseases. Furthermore, in light of the large computational burden incurred by the high-dimensional interactions, we devise a rapid strategy and extend it to a more practical but challenging chromosome-wide setting. We conduct extensive experiments on two databases under three tasks, i.e. multimodal correlation, disease diagnosis, and trait prediction. MA-CBxDP establishes new state-of-the-art results in predicting clinical scores and disease status classification, while maintaining exceptional interpretability, verifying its flexibility and versatility in practical applications.
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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".