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.

翻译：神经结构学习对科学发现与可解释性至关重要。然而，当前聚焦计算资源效率的剪枝算法在筛选符合领域专业知识的有效模型时面临算法层面的障碍。为应对这一挑战，我们提出DASH算法，利用可用的领域特定结构信息指导剪枝过程。在动态基因调控网络模型学习场景中，我们证明DASH结合现有的相互作用伙伴通用知识，能够提供与生物学机制相符的数据特异性洞见。通过基于真实标注的合成数据及两项实际应用案例，我们验证了DASH的有效性：该方法不仅显著优于现有竞争算法，还能提取更具生物学意义的洞察。本研究揭示，领域特定的结构信息具有提升模型科学发现潜力的重要价值。