Hold out the genome: A roadmap to solving the cis-regulatory code
Bibliographic record
Abstract
Abstract Gene expression is regulated by transcription factors that work together to read cis-regulatory DNA sequences. The “cis-regulatory code” - the rules that cells use to determine when, where, and how much genes should be expressed - has proven to be exceedingly complex, but recent advances in the scale and resolution of functional genomics assays and Machine Learning have enabled significant progress towards deciphering this code. However, we will likely never solve the cis-regulatory code if we restrict ourselves to models trained only on genomic sequences; regions of homology can easily lead to overestimation of predictive performance, and there is insufficient sequence diversity in our genomes to learn all relevant parameters. Fortunately, randomly synthesized DNA sequences enable us to test a far larger sequence space than exists in our genomes in each experiment, and designed DNA sequences enable a targeted query of the sequence space to maximally improve the models. Since cells use the same biochemical principles to interpret DNA regardless of its source, models that are trained on these synthetic data can predict genomic activity, often better than genome-trained models. Here, we provide an outlook on the field, and propose a roadmap towards solving the cis-regulatory code by training models exclusively on non-genomic DNA sequences, and using genomic sequences solely for evaluating the resulting models.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".