# Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction

arXiv: https://arxiv.org/abs/2602.18885 · HTML: https://mlsanity.com/paper/2602.18885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling](https://mlsanity.com/paper/2608.06824/) (2026)

## Abstract (arXiv)

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, textscAdaPert outperforms existing baselines, with the largest gains on DEG-aware metrics.