Bayesian Validation of Dynamic Systems for Biological Networks
Bibliographic record
Abstract
Dynamic systems encompass a broad class of mathematical models used to describe the behavior of complex networks or systems over time. One of the most common approaches to modeling such dynamics is through a set of ordinary differential equations (ODEs), typically constructed based on hypotheses, known interactions, or observed trajectories. However, ODEs are deterministic and inflexible, while biological data are typically noisy. Thus, the model fit might not account for all possible data variations, and there might be a discrepancy between the actual biological process and the assumed model. This discrepancy could lead to inaccuracies in the prediction and interpretation of the biological networks. Therefore, it is required to validate ODE models in terms of observed data. Given that biological networks typically involve multiple sources of errors and uncertainties, the validation process should account for these factors. The Bayesian approaches offer a robust framework for quantifying errors and uncertainties. Thus, in this study, we propose a Bayesian validation method for ODE models that addresses model inadequacy, presented as bias. Since the proposed method estimates bias as a function of time, it can provide prediction bounds for the entire observed time interval. Consequently, it allows for a direct evaluation of the model's validity across the whole time interval, and it can lead to better prediction by correcting the bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".