Current Screens Based on the AlphaScreen™ Technology for Deciphering Cell Signalling Pathways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Global deciphering of signal transduction pathways represents a new challenge of the post-genomic era. However, for the majority of these signaling pathways the role(s), the function(s) and the interaction(s) of the signaling intermediates remain to be characterized in an integrated fashion. The global molecular study of cell signaling pathways and networks consequently requires sensitive, robust technologies which may allow in addition multi-parallel and highthroughput applications. The Alphascreen technology, relying on a bead-based homogenous approach, constitutes a valuable tool to detect and quantify a wide range of signaling events such as enzymatic activities or biomolecular interactions. In this article, we exhaustively review the literature and report the broad spectrum of Alphascreen-based applications in the study of signal transduction pathways.
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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