Robust Machine Learning for Regulatory Sequence Modeling under Biological and Technical Distribution Shifts
Read the original on arXiv Statistics ML →The study evaluates the robustness of deep learning models for DNA regulatory sequence prediction under biologically and technically induced distribution shifts. Using a mechanistic simulation benchmark and a massively parallel reporter assay dataset, the authors show that while models perform well under mild GC content changes, they exhibit higher error, miscalibration, and coverage collapse when faced with motif rewiring, noise, and batch effects. Incorporating biological priors and uncertainty-aware selective prediction improves robustness, though protection against strong assay noise remains limited.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.