适应性数据-知识对齐在基因扰动预测中的应用
Adaptive Data-Knowledge Alignment in Genetic Perturbation Prediction
AI总结：
本文提出ALIGNED框架，通过整合数据驱动学习与现有知识，解决基因扰动预测中数据与知识不一致的问题，提升预测一致性与生物知识重构能力。
摘要：
转录响应揭示复杂细胞系统的基本机制。尽管现有方法在预测基因扰动响应方面有所进展，但其生物理解有限且无法系统性地优化现有知识。为克服这些限制，本文提出ALIGNED（适应性对齐遗传知识与数据框架），基于Abductive Learning（ABL）范式，整合神经和符号组件，系统性地优化知识。我们引入平衡一致性度量来评估预测的一致性，结果表明ALIGNED在平衡一致性方面优于现有方法，同时重新发现具有生物学意义的知识。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
