The Labels Run Out Before The Species Do
Methylation predictors have abundant data for a few organisms and scraps for many others. Training a fresh model per species throws away conserved sequence grammar.
methyloadapt creates two source domains and a low-label target domain with a shifted but related methylation motif. The baseline sees six target examples. The adapted model sees the shared domains first.
Transfer The Motif, Not The Answer Sheet
The model learns transparent k-mer evidence. Source species contribute conserved CpG structure; the small target set then reweights what is species-specific.
There is no claim that a two-mer score replaces hierarchical attention. The point is to make domain-adaptive pretraining's advantage visible under controlled label scarcity.
The Number
Target-only training stays at 50% accuracy. Domain-adaptive training reaches 100% on 160 held-out sequences, a 50-point gain.
The dataset is synthetic and non-diagnostic. One deterministic unit test enforces the published benchmark floor in CI.
Research Basis
Inspired by the 2025 iDNA-DAPHA methylation study. The portfolio number above comes from this repository's own controlled benchmark, not from the paper.