Robust Machine Learning for Regulatory Sequence Modeling under Biological and Technical Distribution Shifts
The study investigates how well deep learning models for DNA regulatory sequences perform when faced with real-world biological and technical changes, such as different cell types or sequencing errors. The researchers found that standard evaluation methods miss critical robustness failures in extreme conditions. Incorporating structural biological information and using uncertainty-aware prediction provides a safety net against these distribution shifts.