  Neural structure learning is of paramount importance for scientific discovery
and interpretability. Yet, contemporary pruning algorithms that focus on
computational resource efficiency face algorithmic barriers to select a
meaningful model that aligns with domain expertise. To mitigate this challenge,
we propose DASH, which guides pruning by available domain-specific structural
information. In the context of learning dynamic gene regulatory network models,
we show that DASH combined with existing general knowledge on interaction
partners provides data-specific insights aligned with biology. For this task,
we show on synthetic data with ground truth information and two real world
applications the effectiveness of DASH, which outperforms competing methods by
a large margin and provides more meaningful biological insights. Our work shows
that domain specific structural information bears the potential to improve
model-derived scientific insights.
