When Genomic Masking Priors Fail to Transfer: Strong Variant Prediction, Weak Functional Generation
Read the original on arXiv Machine Learning →The paper introduces GenDA, a bidirectional discrete diffusion model designed for genomic sequence reconstruction, hypothesizing that entropy-guided span placement would improve variant-effect prediction and functional sequence generation. While the 202‑million‑parameter GenDA model achieves a higher ClinVar SNV AUROC (0.774) than a comparable autoregressive model, the improvement is not attributable to entropy guidance, and the model fails to outperform a shuffled‑gap baseline in zero‑shot functional inpainting across various genomic regions. The authors identify limitations such as tokenization granularity, span length caps, and the mismatch between local sequence complexity and functional importance, concluding that variant prediction, corruption priors, and functional generation are distinct tasks requiring separate validation.
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