Improving Detection of Rare Biological Events in High-Throughput Screens
Why this work is in the frame
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Bibliographic record
Abstract
The success of high-throughput screening (HTS) strategies depends on the effectiveness of both normalization methods and study design. We report comparisons among normalization methods in two titration series experiments. We also extend the results in a third experiment with two differently designed but otherwise identical screens: compounds in replicate plates were either placed in the same well locations or were randomly assigned to different locations. Best results were obtained when randomization was combined with normalization methods that corrected for within-plate spatial bias. We conclude that potent, reliable, and accurate HTS requires replication, randomization design strategies, and more extensive normalization than is typically done and that formal statistical testing is desirable. The Statistics and dIagnostic Graphs for HTS (SIGHTS) Microsoft Excel Add-In software is available to conduct most analyses reported here.
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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 | 0.000 | 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 it