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000307519 1001_ $$0P:(DE-He78)a6fb3263ea9078565f7e311b92d355c9$$aDimitrov, Daniel$$b0$$eFirst author$$udkfz
000307519 245__ $$aInterpretation, extrapolation and perturbation of single cells.
000307519 260__ $$aLondon$$bNature Publ. Group$$c2026
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000307519 520__ $$aSingle-cell analyses have transitioned from descriptive atlasing towards inferring causal effects and mechanistic relationships that capture cellular logic. Technological advances and the growing scale of observational and interventional datasets have fuelled the development of machine learning methods aimed at identifying such dependencies and extrapolating perturbation effects. Here, we review and connect these approaches according to their modelling concepts (including representation learning, causal inference, mechanistic discovery, disentanglement and population tracing), underlying assumptions and downstream tasks. We propose a unifying ontology to guide practitioners in selecting the most suitable methods for a given biological question, with detailed technical descriptions provided in an online resource . Finally, we identify promising computational directions and underexplored data properties that could pave the way for future developments.
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000307519 7001_ $$0P:(DE-He78)d41e5650fd2f499002c16564d86f4bbc$$aSchrod, Stefan$$b1$$eFirst author$$udkfz
000307519 7001_ $$0P:(DE-He78)87393f92c9de53503860d2e999f35930$$aRohbeck, Martin$$b2$$udkfz
000307519 7001_ $$0P:(DE-He78)9aabcfee1a1fc9202398a45a63f0b1e3$$aStegle, Oliver$$b3$$eLast author$$udkfz
000307519 773__ $$0PERI:(DE-600)2028884-0$$a10.1038/s41576-025-00920-4$$pnn$$tNature reviews / Genetics$$vnn$$x1471-0056$$y2026
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