Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations

• URL: http://arxiv.org/abs/2507.02980v1
• Date: Tue, 01 Jul 2025 06:04:28 GMT
• Title: Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations
• Authors: Kalyan Ramakrishnan, Jonathan G. Hedley, Sisi Qu, Puneet K. Dokania, Philip H. S. Torr, Cesar A. Prada-Medina, Julien Fauqueur, Kaspar Martens,
• Abstract summary: We train a neural network to predict distributional responses in gene expression following genetic perturbations.<n>Our model predicts gene-level histograms conditioned on perturbations and outperforms baselines in capturing higher-order statistics.
• Score: 44.619690829431214
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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