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<p>Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet, exhaustively exploring the space of possible perturbations (for example, multigene perturbations or across tissues and cell types) is prohibitively expensive, motivating methods that can generalize to unseen conditions. We present TxPert, a latent-transfer-based deep learning method that uses multiple knowledge graphs of gene (product)–gene (product) relationships to predict transcriptomic perturbation effects. Different knowledge graphs encode complementary information and we show that a combination of graphs derived from biological databases and high-throughput perturbation screens yields the best performance. For predictions of single unseen perturbations, TxPert approaches the performance of split-half experimental reproducibility. For double unseen perturbations and single perturbations in a different cell line, its predictions increase Person <i>Δ</i> for unseen single perturbations by 8–25% over existing methods.</p>

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TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月1日

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  • Frederik Wenkel,
  • Wilson Tu,
  • Cassandra Masschelein,
  • Hamed Shirzad,
  • Liam Hodgson,
  • Ihab Bendidi,
  • Cian Eastwood,
  • Shawn T. Whitfield,
  • Craig Russell,
  • Yassir El Mesbahi,
  • Jiarui Ding,
  • Marta M. Fay,
  • Berton Earnshaw,
  • Emmanuel Noutahi,
  • Alisandra K. Denton

摘要

Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet, exhaustively exploring the space of possible perturbations (for example, multigene perturbations or across tissues and cell types) is prohibitively expensive, motivating methods that can generalize to unseen conditions. We present TxPert, a latent-transfer-based deep learning method that uses multiple knowledge graphs of gene (product)–gene (product) relationships to predict transcriptomic perturbation effects. Different knowledge graphs encode complementary information and we show that a combination of graphs derived from biological databases and high-throughput perturbation screens yields the best performance. For predictions of single unseen perturbations, TxPert approaches the performance of split-half experimental reproducibility. For double unseen perturbations and single perturbations in a different cell line, its predictions increase Person Δ for unseen single perturbations by 8–25% over existing methods.