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

arXiv: https://arxiv.org/abs/2505.14919 · HTML: https://mlsanity.com/paper/2505.14919/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 52 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Rethinking Graph Transformers with Spectral Attention](https://mlsanity.com/paper/2106.03893/) (2021)
- [Exphormer: Sparse Transformers for Graphs](https://mlsanity.com/paper/2303.06147/) (2023)
- [Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2206.04216/) (2022)
- [Expander Graph Propagation](https://mlsanity.com/paper/2210.02997/) (2022)
- [How does over-squashing affect the power of GNNs?](https://mlsanity.com/paper/2306.03589/) (2023)
- [Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology](https://mlsanity.com/paper/2404.10242/) (2024)
- [PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis](https://mlsanity.com/paper/2408.10609/) (2024)
- [Cayley Graph Propagation](https://mlsanity.com/paper/2410.03424/) (2024)
- [Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all](https://mlsanity.com/paper/2410.13956/) (2024)
- [Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks](https://mlsanity.com/paper/2201.08932/) (2022)
- [Even Sparser Graph Transformers](https://mlsanity.com/paper/2411.16278/) (2024)
- [A Theory for Compressibility of Graph Transformers for Transductive Learning](https://mlsanity.com/paper/2411.13028/) (2024)

## Built on by (most cited first, 9 of 9)

- [OwkinZero: Accelerating Biological Discovery with AI](https://mlsanity.com/paper/2508.16315/) (2025)
- [STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations](https://mlsanity.com/paper/2602.10156/) (2026)
- [Progressive Multi-Agent Reasoning for Biological Perturbation Prediction](https://mlsanity.com/paper/2602.07408/) (2026)
- [MechPert: Mechanistic Consensus as an Inductive Bias for Unseen Perturbation Prediction](https://mlsanity.com/paper/2602.13791/) (2026)
- [Towards Autonomous Mechanistic Reasoning in Virtual Cells](https://mlsanity.com/paper/2604.11661/) (2026)
- [Adaptive Data-Knowledge Alignment in Genetic Perturbation Prediction](https://mlsanity.com/paper/2510.00512/) (2025)
- [Artificial Intelligence Virtual Cells: From Measurements to Decisions across Modality, Scale, Dynamics, and Evaluation](https://mlsanity.com/paper/2510.12498/) (2025)
- [Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors](https://mlsanity.com/paper/2606.08816/) (2026)
- [PertReason: A Knowledge-Grounded Benchmark and Framework for Cell-State-Conditioned Mechanistic Reasoning of Perturbation Effects](https://mlsanity.com/paper/2607.18777/) (2026)

## Abstract (arXiv)

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.