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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.18885 |
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| _version_ | 1866917286453968896 |
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| author | Piao, Yinhua Kim, Hyomin Kim, Seonghwan Oh, Yunhak Jeon, Junhyeok Hwang, Sang-Yeon Lim, Jaechang Kim, Woo Youn Park, Chanyoung Ahn, Sungsoo |
| author_facet | Piao, Yinhua Kim, Hyomin Kim, Seonghwan Oh, Yunhak Jeon, Junhyeok Hwang, Sang-Yeon Lim, Jaechang Kim, Woo Youn Park, Chanyoung Ahn, Sungsoo |
| contents | Predicting high-dimensional transcriptional responses to genetic perturbations is challenging due to severe experimental noise and sparse gene-level effects. Existing methods often suffer from mean collapse, where high correlation is achieved by predicting global average expression rather than perturbation-specific responses, leading to many false positives and limited biological interpretability. Recent approaches incorporate biological knowledge graphs into perturbation models, but these graphs are typically treated as dense and static, which can propagate noise and obscure true perturbation signals. We propose AdaPert, a perturbation-conditioned framework that addresses mean collapse by explicitly modeling sparsity and biological structure. AdaPert learns perturbation-specific subgraphs from biological knowledge graphs and applies adaptive learning to separate true signals from noise. Across multiple genetic perturbation benchmarks, AdaPert consistently outperforms existing baselines and achieves substantial improvements on DEG-aware evaluation metrics, indicating more accurate recovery of perturbation-specific transcriptional changes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18885 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction Piao, Yinhua Kim, Hyomin Kim, Seonghwan Oh, Yunhak Jeon, Junhyeok Hwang, Sang-Yeon Lim, Jaechang Kim, Woo Youn Park, Chanyoung Ahn, Sungsoo Computational Engineering, Finance, and Science Predicting high-dimensional transcriptional responses to genetic perturbations is challenging due to severe experimental noise and sparse gene-level effects. Existing methods often suffer from mean collapse, where high correlation is achieved by predicting global average expression rather than perturbation-specific responses, leading to many false positives and limited biological interpretability. Recent approaches incorporate biological knowledge graphs into perturbation models, but these graphs are typically treated as dense and static, which can propagate noise and obscure true perturbation signals. We propose AdaPert, a perturbation-conditioned framework that addresses mean collapse by explicitly modeling sparsity and biological structure. AdaPert learns perturbation-specific subgraphs from biological knowledge graphs and applies adaptive learning to separate true signals from noise. Across multiple genetic perturbation benchmarks, AdaPert consistently outperforms existing baselines and achieves substantial improvements on DEG-aware evaluation metrics, indicating more accurate recovery of perturbation-specific transcriptional changes. |
| title | Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2602.18885 |