When Genomic Masking Priors Fail to Transfer: Strong Variant Prediction, Weak Functional Generation

2026-09-04

AI Summary

研究探讨了GenDA模型在基因组序列重建中的应用，尽管其在变异效应预测上表现较好，但在功能序列生成方面未达预期，揭示了熵引导方法的局限性。

Abstract

Bidirectional discrete diffusion model appears naturally suited to genomic modeling because it can reconstruct missing sequence from both flanks. We developed GenDA (Genomic Density-optimized Absorbing Diffusion) under the additional hypothesis that entropy-guided span placement would concentrate reconstruction pressure on compositionally complex regions, improving both downstream variant-effect prediction and functional sequence generation. Our results only partially support this premise. After supervised fine-tuning, the 202M-parameter GenDA model reaches a pooled ClinVar SNV AUROC of 0.774, exceeding a similarly scaled autoregressive model by 0.103. However, a matched random-span variant reaches 0.777, providing no evidence that entropy guidance causes the ClinVar improvement. More unexpectedly, GenDA fails a zero-shot functional inpainting stress test: across promoters, enhancers, exon boundaries, and intron boundaries, it does not consistently outperform a control that shuffles the native gap while exactly preserving 3-mer composition. Failure is already present for 50--500-bp gaps, although enhancer degradation worsens at longer gaps. Diagnostics identify several boundary conditions: entropy measures local sequence complexity rather than functional importance; 1-mer tokenization limits physical context; training spans are capped at 300 bp; and high absolute AlphaGenome fidelity can coexist with negative control-normalized restoration. These results show that strong fine-tuned variant prediction, a plausible corruption prior, and functional generation are distinct claims that require separate validation.

Problem

• Genomic Masking Priors
• Variant Prediction
• Functional Generation
• Entropy Guidance
• Bidirectional Discrete Diffusion

Innovation

• Bidirectional Discrete Diffusion Model
• Entropy-guided Span Placement
• Genomic Modeling
• Variant-effect Prediction
• Functional Sequence Generation