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。

盐胡椒调味品与季节组合干预预测

Season combinatorial intervention predictions with Salt & Peper

Large language models (LLMs) executing tasks through instruction-based
prompts often face challenges stemming from distribution differences between
user instructions and training instructions. This leads to distractions and
biases, especially when dealing with inconsistent dynamic labels. In this
paper, we introduces a novel bias mitigation method, CRISPR, designed to
alleviate instruction-label biases in LLMs. CRISPR utilizes attribution methods
to identify bias neurons influencing biased outputs and employs pruning to
eliminate the bias neurons. Experimental results demonstrate the method's
effectiveness in mitigating biases in instruction-based prompting, enhancing
language model performance on social bias benchmarks without compromising
pre-existing knowledge. CRISPR proves highly practical, model-agnostic,
offering flexibility in adapting to evolving social biases.

该研究介绍了一种用于减轻大型语言模型中指令 - 标签偏见的新的偏见缓解方法，称为 CRISPR，该方法利用属性方法识别有影响的偏见神经元并通过修剪来消除这些偏见神经元。实验结果表明，CRISPR 在减轻指令 - 标签偏见方面非常有效，在社交偏见基准上提高了语言模型的性能，同时不损害现有知识。CRISPR 是高度实用和模型无关的，具有在应对不断演变的社交偏见中灵活性的特点。