Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations

Kalyan Ramakrishnan; Jonathan G. Hedley; Sisi Qu; Puneet K. Dokania; Philip H. S. Torr; Cesar A. Prada-Medina; Julien Fauqueur; Kaspar Martens

arXiv · 2507.02980

Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations

Kalyan Ramakrishnan·Jonathan G. Hedley·Sisi Qu·Puneet K. Dokania·Philip H. S. Torr·Cesar A. Prada-Medina·Julien Fauqueur·Kaspar Martens

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Abstract

We train a neural network to predict distributional responses in gene expression following genetic perturbations. This is an essential task in early-stage drug discovery, where such responses can offer insights into gene function and inform target identification. Existing methods only predict changes in the mean expression, overlooking stochasticity inherent in single-cell data. In contrast, we offer a more realistic view of cellular responses by modeling expression distributions. Our model predicts gene-level histograms conditioned on perturbations and outperforms baselines in capturing higher-order statistics, such as variance, skewness, and kurtosis, at a fraction of the training cost. To generalize to unseen perturbations, we incorporate prior knowledge via gene embeddings from large language models (LLMs). While modeling a richer output space, the method remains competitive in predicting mean expression changes. This work offers a practical step towards more expressive and biologically informative models of perturbation effects.

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Kalyan Ramakrishnan, Jonathan G. Hedley, Sisi Qu, Puneet K. Dokania, Philip H. S. Torr, Cesar A. Prada-Medina, Julien Fauqueur, Kaspar Martens. 2025-07-01. Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations. https://arxiv.org/abs/2507.02980

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