Beyond Fitting: Knowledge-Guided LLM Reasoning for Combinatorial Gene Perturbation Prediction

Combinatorial perturbation prediction aims to estimate transcriptional responses induced by simultaneous perturbation of multiple genes, which is essential for advancing a mechanistic understanding of biochemical processes. Existing deep learning methods primarily rely on learned representations to predict perturbation responses, often lacking explicit reasoning and mechanistic interpretability. Meanwhile, emerging LLM-enhanced methods still struggle with continuous-response prediction and inductive generalization to unseen perturbation genes. To address these limitations, we propose **CombPert**, a biological knowledge-guided LLM reasoning framework for **Comb**inatorial **Pert**urbation prediction. Specifically, CombPert first constructs a task-specific biological knowledge graph from curated biological resources and dataset-specific co-expression statistics, and retrieves mechanistic paths connecting the perturbation genes and the target gene. To improve inductive generalization and response magnitude estimation, CombPert further incorporates similarity-based evidence from similar perturbation genes and conditional statistical priors. Finally, CombPert integrates these sources into a structured CoT prompt, supporting Reasoning Elicitation for training-free prediction and mechanistic explanation, and Reasoning Synthesis for CoT data generation and downstream fine-tuning. Extensive experiments on representative benchmarks demonstrate strong predictive performance and inductive generalization to unseen perturbation genes. Our code is available at https://anonymous.4open.science/r/CombPert-submit-FBA5.