TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction

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 (e.g., multi-gene perturbations or across tissues and cell types) is prohibitively expensive, motivating methods that can generalize to unseen conditions. In this work, we explore how knowledge graphs of gene-gene relationships can improve out-of-distribution (OOD) prediction across three challenging settings: unseen single perturbations; unseen double perturbations; and unseen cell lines. In particular, we present: (i) TxPert, a new state-of-the-art method that leverages multiple biological knowledge networks to predict transcriptional responses under OOD scenarios; (ii) an in-depth analysis demonstrating the impact of graphs, model architecture, and data on performance; and (iii) an expanded benchmarking framework that strengthens evaluation standards for perturbation modeling.

@article{wenkel2025_2505.14919,
  title={ TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction },
  author={ Frederik Wenkel and Wilson Tu and Cassandra Masschelein and Hamed Shirzad and Cian Eastwood and Shawn T. Whitfield and Ihab Bendidi and Craig Russell and Liam Hodgson and Yassir El Mesbahi and Jiarui Ding and Marta M. Fay and Berton Earnshaw and Emmanuel Noutahi and Alisandra K. Denton },
  journal={arXiv preprint arXiv:2505.14919},
  year={ 2025 }
}