Dynamic sensor selection for biomarker discovery
Bibliographic record
Abstract
Recent advances in biotechnologies enable monitoring of biological systems with unprecedented resolution, yet identifying and interpreting biological signals remains a major challenge in clinical and research settings. Classically, biomarkers are measurable indicators of the state of biological processes. Given the large number of molecules in modern datasets, a major challenge is identifying the best biomarkers for a particular setting. Here, we apply observability theory to establish a general methodology for biomarker selection. We demonstrate that observability identifies biologically meaningful sensors in a range of time series transcriptomics data. To address unique biological constraints, we introduce the method of dynamic sensor selection to maximize observability over time, thus enabling observability over regimes where system dynamics themselves are subject to change. Our observability-guided biomarker discovery framework extends to multiple data modalities, as demonstrated with the joint use of transcriptomics and chromosome conformation data. We demonstrate the generality of this approach by evaluating the observability of neural activity measured in movies and electroencephalograms. These applications highlight the broad utility of observability-guided biomarker selection, spanning agriculture, biomanufacturing, and neural systems.
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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".