Uncovering Non-Monotonic Antagonistic and Synergistic Combinations (UNMASC), a robust method with applications to T cell differentiation <i>in vitro</i>
Bibliographic record
Abstract
ABSTRACT Induced pluripotent stem cells (iPSCs) have been proposed as an alternative T cell source for CAR-T cell therapies, as they can be differentiated and matured into T cells in vitro using cytokines. These assays benefit from computational and mathematical models to design appropriate experimental protocols. However, models are limited by typical monotonic dose-responses, preventing them from being used for cytokine effects that are largely non-monotonic. To address this shortcoming, we developed Uncovering Non-Monotonic Antagonistic and Synergistic Combinations (UNMASC), a novel mathematical model describing non-monotonic dose-response surfaces of cytokine interactions that distinguishes synergy of efficacy and potency. We showed that our approach successfully recapitulates non-monotonic observed dose-response surfaces characterizing in vitro T cell progenitor differentiation. Our results highlighted cytokine combinations with antagonistic effects alone but synergistic in combination, particularly IL3 and IL7. Together, UNMASC accelerates the efficient cell generation assays and extends drug interaction surfaces to a broader range of dose-responses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".