Interventions play a pivotal role in the study of complex biological systems.
In drug discovery, genetic interventions (such as CRISPR base editing) have
become central to both identifying potential therapeutic targets and
understanding a drug's mechanism of action. With the advancement of CRISPR and
the proliferation of genome-scale analyses such as transcriptomics, a new
challenge is to navigate the vast combinatorial space of concurrent genetic
interventions. Addressing this, our work concentrates on estimating the effects
of pairwise genetic combinations on the cellular transcriptome. We introduce
two novel contributions: Salt, a biologically-inspired baseline that posits the
mostly additive nature of combination effects, and Peper, a deep learning model
that extends Salt's additive assumption to achieve unprecedented accuracy. Our
comprehensive comparison against existing state-of-the-art methods, grounded in
diverse metrics, and our out-of-distribution analysis highlight the limitations
of current models in realistic settings. This analysis underscores the
necessity for improved modelling techniques and data acquisition strategies,
paving the way for more effective exploration of genetic intervention effects.

通过对基因干预的影响进行估计，本研究聚焦于基因对的组合对细胞转录组的效应，并引入了 Salt 和 Peper 两种新方法，Salt 从生物学启发并假设组合效应主要是加性的，而 Peper 则通过深度学习拓展了 Salt 的加性假设以实现前所未有的准确性，与现有先进方法进行全面比较并在现实环境中进行越界分析突出了当前模型的局限性，凸显了改进建模技术和数据获取策略的必要性，为更有效地探索基因干预效应铺平了道路。