Quantitative predictions for DNA two‐dimensional display according to size and nucleotide sequence composition
Bibliographic record
Abstract
2-D DNA display is a simple separation method that provides a fast and economical way of visualizing polymorphism and comparing genomes. The DNA fragments are separated first according to their size by standard gel electrophoresis and then according to their sequence composition using denaturing gradient gel electrophoresis. First developed by Fischer and Lerman (Cell 1979, 16, 191-200), this method has recently been used to distinguish strains within a bacterial species. The genomic restriction fragments are displayed as spots on a 2-D surface. Although most of the relevant physical mechanisms are understood, this technique is mostly empirical and remains essentially qualitative. In view of optimizing this procedure, we combine our understanding of the different physical mechanisms at play to develop a complete numerical model to predict the relative coordinates of the spots as a function of the corresponding DNA sequence and of the experimental conditions. We experimentally validate our model by predicting the outcome of a 2-D display of the lambda phage genome. It thus becomes possible to optimize in silico the experimental parameters, to predict whether specific mutations as well as yet undescribed genetic polymorphisms can be resolved, and to assist in interpreting the experimental data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".