A web‐tool for visualizing quantitative protein–protein interaction data
Why this work is in the frame
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Bibliographic record
Abstract
Quantitative interaction proteomics data can be a challenge to efficiently analyze and subsequently present to an audience in a simple and easy to understand format that still conveys sufficient levels of information. Here we present freely accessible and open-source web tools for displaying multiple parameters from quantitative protein-protein interaction data sets in a visually intuitive format. Given a set of "bait" proteins with detected "prey" interactions, dot plots can be generated to display absolute spectral counts for the preys, relative spectral counts between baits and confidence levels for the interactions (e.g. as determined by SAINTexpress). Additional tools are available for displaying fold change results between numerous baits with their associated confidence level (e.g. resulting from intensity measurements) and pairwise bait analyses displaying spectral counts, confidence score and fold change differences in a scatter plot format. These tools make it easy for the user to identify important interaction changes, interpret their data, and present this information to others in an intuitive way.
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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 | 0.001 |
| 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