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

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arxiv 2505.14919 v1 pith:SPIQXI2P submitted 2025-05-20 cs.LG q-bio.QM

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

Frederik Wenkel , Wilson Tu , Cassandra Masschelein , Hamed Shirzad , Cian Eastwood , Shawn T. Whitfield , Ihab Bendidi , Craig Russell
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Liam Hodgson Yassir El Mesbahi Jiarui Ding Marta M. Fay Berton Earnshaw Emmanuel Noutahi Alisandra K. Denton
This is my paper
classification cs.LG q-bio.QM
keywords perturbationsunseenacrosscellgraphsknowledgeout-of-distributionperturbation
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. PertReason: A Knowledge-Grounded Benchmark and Framework for Cell-State-Conditioned Mechanistic Reasoning of Perturbation Effects

    cs.LG 2026-07 conditional novelty 6.0

    PertReasonQA scores AI models on cell-state-conditioned mechanistic reasoning about perturbation effects, and PertReasonLM, trained with reasoning supervision, reaches 0.736 balanced accuracy and 0.976 edge recall ver...

  2. What Makes a Representation Good for Single-Cell Perturbation Prediction?

    cs.LG 2026-05 unverdicted novelty 6.0

    PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.

  3. Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction

    cs.CE 2026-02 conditional novelty 6.0

    AdaPert improves transcriptional perturbation prediction by conditioning sparse knowledge-graph subgraphs on each perturbed gene and training with signal/noise separation losses, achieving the best DEG recovery on K56...

  4. Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors

    cs.LG 2026-06 unverdicted novelty 5.0

    K-nearest neighbor from a knowledge graph beats most methods on out-of-distribution transcriptomic perturbation prediction, and an RL-trained reasoning LLM matches SOTA on Replogle et al. (2022) cell lines while impro...