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

• URL: http://arxiv.org/abs/2505.14919v1
• Date: Tue, 20 May 2025 21:13:23 GMT
• Title: TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction
• Authors: Frederik Wenkel, Wilson Tu, Cassandra Masschelein, Hamed Shirzad, Cian Eastwood, Shawn T. Whitfield, Ihab Bendidi, Craig Russell, Liam Hodgson, Yassir El Mesbahi, Jiarui Ding, Marta M. Fay, Berton Earnshaw, Emmanuel Noutahi, Alisandra K. Denton,
• Abstract summary: We present TxPert, a new state-of-the-art method that leverages multiple biological knowledge networks to predict responses under OOD scenarios.<n>In particular, we present: (i) TxPert, a new state-of-the-art method that leverages multiple biological knowledge networks to predict 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.
• Score: 11.083533122552396
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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