Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911038124851200 |
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| author | Ramakrishnan, Kalyan Hedley, Jonathan G. Qu, Sisi Dokania, Puneet K. Torr, Philip H. S. Prada-Medina, Cesar A. Fauqueur, Julien Martens, Kaspar |
| author_facet | Ramakrishnan, Kalyan Hedley, Jonathan G. Qu, Sisi Dokania, Puneet K. Torr, Philip H. S. Prada-Medina, Cesar A. Fauqueur, Julien Martens, Kaspar |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_02980 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations Ramakrishnan, Kalyan Hedley, Jonathan G. Qu, Sisi Dokania, Puneet K. Torr, Philip H. S. Prada-Medina, Cesar A. Fauqueur, Julien Martens, Kaspar Genomics Machine Learning 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. |
| title | Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations |
| topic | Genomics Machine Learning |
| url | https://arxiv.org/abs/2507.02980 |