标题： 遗传扰动预测中的自适应数据知识对齐 显示英文标题

标题： Adaptive Data-Knowledge Alignment in Genetic Perturbation Prediction

摘要： 转录反应对基因扰动的揭示为复杂细胞系统提供了基本的见解。 虽然当前的方法在预测基因扰动反应方面取得了一步进展，但它们提供的生物学理解有限，并且不能系统地改进现有知识。 克服这些限制需要数据驱动的学习和现有知识的端到端整合。 然而，由于数据和知识库之间存在不一致，例如噪声、错误注释和不完整性，这种整合具有挑战性。 为了解决这个挑战，我们提出了 ALIGNED（自适应对齐用于不一致的遗传知识和数据），一个基于归结学习（ABL）范式的神经符号框架。 这个端到端框架对齐神经和符号组件并执行系统性的知识改进。 我们引入了一个平衡的一致性度量来评估预测 against 数据和知识的一致性。 我们的结果表明，ALIGNED 通过达到最高的平衡一致性超越了最先进的方法，同时重新发现了生物上有意义的知识。 我们的工作超越了现有方法，实现了机制性生物学理解的透明性和演化。 显示英文摘要

摘要： The transcriptional response to genetic perturbation reveals fundamental insights into complex cellular systems. While current approaches have made progress in predicting genetic perturbation responses, they provide limited biological understanding and cannot systematically refine existing knowledge. Overcoming these limitations requires an end-to-end integration of data-driven learning and existing knowledge. However, this integration is challenging due to inconsistencies between data and knowledge bases, such as noise, misannotation, and incompleteness. To address this challenge, we propose ALIGNED (Adaptive aLignment for Inconsistent Genetic kNowledgE and Data), a neuro-symbolic framework based on the Abductive Learning (ABL) paradigm. This end-to-end framework aligns neural and symbolic components and performs systematic knowledge refinement. We introduce a balanced consistency metric to evaluate the predictions' consistency against both data and knowledge. Our results show that ALIGNED outperforms state-of-the-art methods by achieving the highest balanced consistency, while also re-discovering biologically meaningful knowledge. Our work advances beyond existing methods to enable both the transparency and the evolution of mechanistic biological understanding.