Phase-Incremental Decision Trees for Multi-Phase Feature Selection and Interaction in Biologics Manufacturing
Why this work is in the frame
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Bibliographic record
Abstract
Data from cell culture processes contain myriad parameters arriving sequentially in phases which may hold vital information for optimizing process runs and ameliorating manufacturing yield. This study analyzed temporal process data from 249 cell culture production batches of an active pharmaceutical ingredient at Roche's Location A manufacturing facility. The titer manufactured is utilized for Roche's Product X, a prescription drug that can treat adults with cancer. We aim to optimize the upstream production phase titer in Chinese hamster ovary cell manufacturing by identifying the most influential features. A phase-incremental (PI) decision tree method is proposed for feature selection and interaction exploration, being model and loss function agnostic while promoting early feature importance for prediction and process control. In this case study, the method is applied to Ensemble of Gradient Boosting Machines, using adjusted R-squared as the penalized loss function. The result leads to better process understanding and enables earlier control in the manufacturing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it