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

2025-05-20

AI Summary

This study addresses the challenge of improving out-of-distribution (OOD) generalization for predicting transcriptional responses to genetic perturbations—specifically, unseen single- and double-gene perturbations and novel cell lines. To overcome the limited experimental coverage that constrains existing methods, we introduce, for the first time, multi-source biological knowledge graphs to guide OOD modeling, establishing a rigorous benchmark framework that enforces strict cross-perturbation-type and cross-cell-line generalization. Our method integrates graph neural networks for knowledge graph encoding, multi-relational heterogeneous graph aggregation, OOD-aware training, and an interpretable attention mechanism. Across all three OOD settings, TxPert achieves a mean R² improvement of 12.7% over state-of-the-art baselines. We publicly release both the new benchmark and the model implementation.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Predicting cellular responses to unseen genetic perturbations
• Improving out-of-distribution prediction using gene-gene knowledge graphs
• Enhancing perturbation modeling evaluation standards

Innovation

• Leverages gene-gene knowledge graphs
• Predicts OOD transcriptional responses
• Uses multiple biological knowledge networks