Enigma: An Efficient Model for Deciphering Regulatory Genomics
Bibliographic record
Abstract
Abstract Genomic sequence-to-function models have emerged as powerful tools for deciphering cis-regulatory grammar to advance our understanding of disease biology and guide therapeutic development. Recent advances have been driven by multi-task training of large transformer-based models on thousands of genome tracks. However, these performance gains have come at significant computational cost for both training and inference, hindering large-scale applications and slowing future model development. Here, rather than continuing to scale model size and add more training tracks, we focus on architectural efficiency and train on a substantially smaller, curated set of genome tracks. Our model Enigma achieves competitive performance with current state-of-the-art models at single-base resolution while substantially reducing computational cost. On zero-shot variant effect prediction benchmarks, Enigma outperforms the leading open-source model, the Borzoi ensemble, while using 10.9% of its compute and improving resolution from 32 bases to a single base. Compared to AlphaGenome, Enigma achieves 90.4 - 97.3% of its performance using 7.5% of its estimated compute. These improvements in efficiency can facilitate further development of models for regulatory genomics. We demonstrate this by fine-tuning Enigma on predicting three new molecular phenotypes — ChIP-seq, RNA half-life, and translation efficiency — achieving or exceeding the performance of state-of-the-art task-specific models. We are providing Enigma for non-commercial use to benefit the broader research field. Code: https://github.com/deepgenomics/enigma
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".