Oyster: a neural network for modelling genomic sequences that enables exact position-specific <i>k</i> -mer contributions
Bibliographic record
Abstract
Abstract Genomic functions arise from nucleotide sequences and their overlapping k -mers – subsequences whose contributions depend on their composition, position and associations. Understanding these contributions requires computing a k -mer contribution function that may or may not consider k -mer associations. Neural networks that model associations yield powerful predictors but are notoriously hard to interpret; conversely, models that ignore associations deliver exact, position-specific contributions yet might underperform. We introduce Oyster, the first convolutional architecture that can be toggled between Exact (ignoring associations) and non-Exact modes. Exact Oysters yield closed-form k -mer contributions directly from their weights, without post-hoc attribution. By letting users choose between interpretability and complexity, Oyster provides a unified framework for transparent, high-performance sequence-to-function modeling. We apply Oyster to predict intensities of YY1-DNA interactions in human K562 cells from 500-nt DNA windows and intensities of eleven histone post-translational modifications. Exact and non-Exact variants achieved statistically indistinguishable performance, highlighting that k -mer associations are not necessarily important for all biological phenomena. Modelling of YY1-DNA interactions is dependent on the YY1 motif which is expected but is also dependent on several histone post-translational modifications including H3K9ac and H2AFZ.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".