Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction
Improves robustness and interpretability of perturbation response prediction for biologists studying genetic perturbations.
AdaPert addresses mean collapse in transcriptional response prediction by extracting sparse, perturbation-specific subgraphs and suppressing noise in non-responsive genes, achieving state-of-the-art performance on DEG-aware metrics across multiple benchmarks.
Predicting high-dimensional transcriptional responses to genetic perturbations is challenging because signals are sparse and experimental noise is severe. Existing methods often suffer from mean collapse, achieving high correlation by predicting the global average expression rather than perturbation-specific responses, which yields false positives and poor interpretability. Methods that add biological knowledge graphs typically treat them as dense, static priors shared across perturbations, propagating noise. We propose AdaPert, which counters mean collapse by extracting a sparse, perturbation-specific subgraph via differentiable node selection, then suppressing spurious variation in non-responsive genes while emphasizing differentially expressed ones. Across multiple benchmarks, \textsc{AdaPert} outperforms existing baselines, with the largest gains on DEG-aware metrics.