SSINCC: Simple separation of interacting nucleoprotein complex components
Bibliographic record
Abstract
Protein-DNA binding assays have been used in a va-riety of applications from fundamental studies re-garding the binding process itself to serve as probes for the detection, quantification and separation of target analytes. Here we describe a novel method of analyzing and identifying intermolecular DNA interactions that allows for the simple separation of interacting nucleoprotein complex components (SSINCC), focusing specifically on DNA-DNA interactions using P1 plasmid active partition system nucleoprotein complexes as a model to demonstrate DNA sequence specificity and tolerance of composite factor complexity. Traditional and recent assays of protein-DNA interaction are summarized and compared with SSINC. Although SSINC is examined here employing P1 partition nucleoprotein complex as an example of DNA-DNA intermolecular association, universal applications of this methodology to nucleo-protein complex studies can be envisioned.
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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".