Visual-SELEX: a technology ensemble for evaluating aptamer structural similarity via 3D visual spatial conformational analysis
Bibliographic record
Abstract
To date, the study of single-stranded DNA (ssDNA) similarity has focused mainly on the similarity of bases in the same position in the nucleic acid sequence. However, focusing only on the similarity of base sequences has limitations. This similarity evaluation considers only the one-dimensional similarity of ssDNA and cannot fully capture the three-dimensional (3D) structural consistency of aptamers for nucleic acids with 3D structures. Therefore, it is necessary to develop a program that can quickly and accurately evaluate the 3D spatial consistency of ssDNA. To this end, we designed a Visual-SELEX rapid response program, which uses a screening ssDNA sequence set enriched in the DKK1 protein for analysis. The program directly generates a stable 3D structure of ssDNA through coarse-grained simulation and molecular dynamics (MD) simulation, converts the structure into a point cloud model, and then analyzes the similarity of the spatial structure of ssDNA through point cloud model alignment and superposition. The analysis results show that Visual-SELEX can accurately match ssDNAs with dissimilar base fragments but similar 3D spatial structures, providing richer 3D spatial similarity information than sequence similarity comparison alone.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".