Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines
Recent research in deep-learning-based foundation models promises to learn representations of single-cell data that enable prediction of the effects of genetic perturbations. Here we compared five foundation models and two other deep learning models against deliberately simple baselines for predicti...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
4 August 2025
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| In: |
Nature methods
Year: 2025, Jahrgang: 22, Heft: 8, Pages: 1657-1661 |
| ISSN: | 1548-7105 |
| DOI: | 10.1038/s41592-025-02772-6 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41592-025-02772-6 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41592-025-02772-6 
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| Verfasserangaben: | Constantin Ahlmann-Eltze, Wolfgang Huber, and Simon Anders |
| Zusammenfassung: | Recent research in deep-learning-based foundation models promises to learn representations of single-cell data that enable prediction of the effects of genetic perturbations. Here we compared five foundation models and two other deep learning models against deliberately simple baselines for predicting transcriptome changes after single or double perturbations. None outperformed the baselines, which highlights the importance of critical benchmarking in directing and evaluating method development. |
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| Beschreibung: | Gesehen am 03.12.2025 |
| Beschreibung: | Online Resource |
| ISSN: | 1548-7105 |
| DOI: | 10.1038/s41592-025-02772-6 |