Quantitative Characterization of Partitioning Stringency in SELEX
Bibliographic record
Abstract
Maintaining stringent conditions in SELEX (Systematic Evolution of Ligands by EXponential enrichment) is crucial for obtaining high-affinity aptamers. However, excessive stringency greatly increases the risk of SELEX failure. Controlling stringency has remained a technical challenge, largely dependent on intuition, due to the absence of a clear, quantitative measure of stringency. This study was motivated by our insight that, while stringency is influenced by multiple factors, it can be quantified by its effect: increasing stringency reduces the quantity of binders normalized to that of nonbinders after partitioning. Based on this insight, we propose measuring stringency using the binder-to-nonbinder ratio (BNR), where a lower BNR indicates higher stringency. We derive an experimental method for determining BNR via quantitative PCR. Our theoretical analysis and SELEX experiments using two distinct proteins as selection targets underscore the importance of maintaining a BNR significantly greater than zero to avoid failure, a principle we call the SELEX nonfailure criterion. By employing inverse BNR to quantify stringency and applying this criterion, researchers can more rationally control SELEX progress. The quantitative stringency measure and nonfailure criterion can also be applied to other artificial evolution methods, provided that selected binders are quantifiable.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".