HiDiNeu: Accelerating Differential-evolution with Artificial Neural Network Predictions towards Regions of Desirable Chemically-defined Cell Culture Formulations
Bibliographic record
Abstract
Addressing the need for Quality-by-Design and full automation of the cell manufacturing pipeline is necessary to meet the rapidly growing demand for clinical grade cell culture media. In this study, the performance of Differential Evolution (DE) combined with Artificial Neural Networks (ANNs) was evaluated in silico on different benchmark functions with response surfaces representing combined effects of factors on cell expansion in culture. Each benchmark function’s known numerical solution (i.e., maxima) was used as a reference, evaluating performance of search algorithm variants by calculating the discrepancy (i.e., error) between the reference and the solution found. The study revealed that when ANNs were incorporated into DE, the solution error decreased by at least 48% and up to 74% compared with DE alone. These results indicate that a DE algorithm with ANN modelling capabilities can identify better final medium formulations supporting in vitro cell manufacturing and reduce experimental costs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".