A Comparison of ALPHAScreen, TR-FRET, and TRF as Assay Methods for FXR Nuclear Receptors
Bibliographic record
Abstract
New developments in detection technologies are providing a variety of biomolecular screening strategies from which to choose. Consequently, we performed a detailed analysis of both separation-based and non-separation-based formats for screening nuclear receptor ligands. In this study, time-resolved fluorescence resonance energy transfer (TR-FRET), ALPHAScreen, and time-resolved fluorescence (TRF) assays were optimized and compared with respect to sensitivity, reproducibility, and miniaturization capability. The results showed that the ALPHAScreen system had the best sensitivity and dynamic range. The TRF assay was more time consuming because of the number of wash steps necessary. The TR-FRET assay had less interwell variation, most likely because of ratiometric measurement. Both the ALPHAScreen and the TR-FRET assays were miniaturized to 8-microl volumes. Of the photomultiplier tube-based readers, the ALPHAScreen reader (ALPHAQuest) presented the advantage of faster reading times through simultaneous reading with four photomultiplier tubes.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".