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<p>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.</p>

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Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月4日

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  • Constantin Ahlmann-Eltze,
  • Wolfgang Huber,
  • Simon Anders

摘要

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.