Editorial: Integration of Machine Learning and Computer Simulation in Solving Complex Physiological and Medical Questions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This Research Topic, "Integration of Machine Learning and Computer Simulation in Solving Complex Physiological and Medical Questions", brings together two powerful computational approaches to investigate complex disease processes: the use of high-fidelity, mechanism-based simulation models (MSMs), and the training of artificial neural networks (ANNs) via machine learning (ML) and artificial intelligence (AI). These two approaches represent distinct aspects of the scientific process: ML/AI involves correlation identification/hypothesis generation whereas MSMs provide an in silico means for hypothesis testing and conceptual model verification, with capabilities that can complement and address each other's limitations. High-fidelity MSMs can contain very large numbers of parameters, which poses challenges to effective parameterization and/or parameter space exploration, and can present prohibitive computational costs in terms of executing simulation experiments. Alternatively, ML/AI approaches are notoriously data-hungry (a considerable issue when dealing with biological data sets that are generally orders of magnitude more sparse compared to other ML applications), are highly limited in terms of testing inferred causal relationships, and are often "black boxes" in terms of interpreting why the ANNs do what they do. This Research Topic brings together work that integrates MSM and ML in a complementary fashion. We have organized these papers in the following general classes of investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| 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 it