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. The control of stringency remains a technical challenge reliant solely on intuition, largely due to the absence of a measure of stringency. Essentially, researchers increase or decrease stringency through its influencers without defining and quantitating it. This study was motivated by our insight that while stringency may not be easily definable via its multiple influencers, it can be delineated by its effect: increasing stringency leads to a decrease in the normalized quantity of binders at the output of partitioning. Building on this insight, we introduce a measure of stringency called the Binder-to-Nonbinder Ratio (BNR) and derive an expression for its experimental determination using a single experimental tool: quantitative PCR. The outcomes of our theoretical analysis and the result of SELEX experiments targeting three distinct protein targets underscore the importance of maintaining a BNR significantly greater than zero to avoid SELEX failure due to excessive stringency – a principle that we term the SELEX non-failure criterion. Utilizing BNR as a measure of stringency alongside this criterion will enable researchers to rationally control SELEX progress.
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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 | 0.006 | 0.016 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".