Generative adversarial networks for the simulation of DNA sequence evolution
Bibliographic record
Abstract
Motivation: Sequence evolution models are at the heart of bioinformatics.They play a crucial role in many of its fundamental problems, including sequence alignment, phylogenetic inference, and ancestral genome reconstruction.While mutation rates at a sequence position are known to depend on its flanking positions, accurately incorporating these context dependencies into realistic sequence evolution models remains challenging. Results:We propose the first generative adversarial network (GAN) approach to automatically learn, in an unsupervised manner, the parameters and weights involved in modeling context-dependent DNA sequence evolution.We exploit a long short-term memory network architecture for both the generator and critic, trained within the framework of a conditional Wasserstein GAN with gradient penalty.We show that the model captures contextual sequence information using various small context sizes.Different strategies to stabilize and accelerate training are discussed.We believe these results open the door for the exploration of more complex network architectures that leverage the state-of-the-art in both GAN and natural language processing research.i
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".