Bibliographic record
Abstract
This poster was presented at PyData Global 2020 and demonstrates a three-step computational workflow for modelling ion channel gating kinetics using Julia and Python. The biological system used is the pacemaker HCN (Hyperpolarization-activated, Cyclic Nucleotide-gated) channel, which regulates rhythmic activity in the heart and brain. The workflow consists of: (1) numerical simulation of a four-state allosteric gating model (CR ↔ CA, OR ↔ OA) using DifferentialEquations.jl; (2) global optimization of model parameters against patch-clamp electrophysiology data using BlackBoxOptim.jl and GCMAES.jl with simultaneous calibration across multiple datasets; and (3) Markov chain Monte Carlo (MCMC) sampling with PyMC3 to approximate the posterior distribution of model parameters and quantify uncertainty. Model identifiability is assessed by evaluating predictive performance on held-out validation datasets. The poster is intended for a data science audience and emphasizes the practical use of the Julia/Python scientific ecosystem for biophysical modelling. Electrophysiology data shown are unpublished; access to underlying data is available upon reasonable request.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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 source (direct Gemma or distilled Codex), 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".