Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings.

Gene perturbation experiments followed by transcriptomic profiling are vital for uncovering causal gene effects. However, their limited throughput leaves many perturbations of interest unexplored. Computational methods are therefore needed to predict genome-wide transcriptional responses to gene perturbations that were not experimentally assayed within a given dataset. Existing approaches often rely on Gene Ontology graphs to encode prior knowledge, but their predictive power and applicability are constrained by the graphs' sparsity and incomplete gene coverage. Here we present Scouter, a computational method that uses gene embeddings generated by large language models and a lightweight compressor-generator neural network. Scouter accurately predicts transcriptional responses to both single- and two-gene perturbations, reducing errors from state-of-the-art Gene Ontology-term-based methods (GEARS and biolord) by half or more. Unlike recent approaches based on fine-tuning gene expression foundation models, Scouter offers substantially better accuracy and greater accessibility; it requires no pretraining and runs efficiently on standard hardware.