Supervised Machine Learning for Bioelectrical Cellular Networks
Bibliographic record
Abstract
1. Abstract Cells utilize bioelectricity to form networks as well as regulate and control a variety of processes such as apoptosis, tumor suppression, and voltage-gated ion channels. In-silico modeling of bioelectrical networks can be performed using BETSE, an application that models gap junctions and ion channel activity of networked cells, but its usage of matrix-based differential equations to estimate these properties limits simulations based on the amount of computational resources available. To alleviate this issue, we trained a total of 8 machine learning models to replace three core functions of BETSE, that is, 1) predicting the average transmembrane potential (V mem ) of an entire cellular network, 2) predicting the V mem of each individual cell within the network, and finally, 3) predicting the average ion concentrations of sodium, potassium, chloride, and calcium within the cell network. For objective 1, the random forest model was shown to be most performant over all 4 scoring metrics, in objective 2 both the decision tree and k-nearest neighbors models scored best in half of all metrics, and for objective 3 the super learner, a meta-learner comprised of multiple base learners, scored best among all scoring metrics. Overall, these models provide a more resource efficient method of predicting properties of bioelectric cellular networks, and future work will include further properties such as temperature and pressure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".