Pooled Screening for Synergistic Interactions Subject to Blocking and Noise
Bibliographic record
Abstract
The complex molecular networks in the cell can give rise to surprising interactions: gene deletions that are synthetically lethal, gene overexpressions that promote stemness or differentiation, synergistic drug interactions that heighten potency. Yet, the number of actual interactions is dwarfed by the number of potential interactions, and discovering them remains a major problem. Pooled screening, in which multiple factors are simultaneously tested for possible interactions, has the potential to increase the efficiency of searching for interactions among a large set of factors. However, pooling also carries with it the risk of masking genuine interactions due to antagonistic influence from other factors in the pool. Here, we explore several theoretical models of pooled screening, allowing for synergy and antagonism between factors, noisy measurements, and other forms of uncertainty. We investigate randomized sequential designs, deriving formulae for the expected number of tests that need to be performed to discover a synergistic interaction, and the optimal size of pools to test. We find that even in the presence of significant antagonistic interactions and testing noise, randomized pooled designs can significantly outperform exhaustive testing of all possible combinations. We also find that testing noise does not affect optimal pool size, and that mitigating noise by a selective approach to retesting outperforms naive replication of all tests. Finally, we show that a Bayesian approach can be used to handle uncertainty in problem parameters, such as the extent of synergistic and antagonistic interactions, resulting in schedules for adapting pool size during the course of testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 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".