arXiv AI September 2, 2026 By Handong Wang, Jiaxin Qi, Haochen Feng, Baisheng Lai

PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction

PopPert is a framework that models population-level joint gene expression distributions to predict transcriptional responses to perturbations in single-cell RNA sequencing data. By using a low‑rank Gaussian Copula, it captures gene co‑expression patterns and eliminates the need for cell‑to‑cell correspondence, thereby reducing sensitivity to single‑cell noise. Across multiple benchmarks, PopPert outperforms existing methods in differential expression recovery, perturbation effect estimation, and distribution matching, demonstrating the effectiveness of population‑level joint distribution learning for unpaired single‑cell data.

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benchmarks

arXiv AI

Aug 25

DeMixPert: Decomposed Response Modeling with Gaussian Mixtures for OOD Single-Cell Perturbation Prediction

arXiv:2608.23114v1 Announce Type: cross Abstract: Predicting transcriptome-wide responses to unseen genetic perturbations remains a major computational challenge because accurate prediction requires...

By Jiawen Liu, Xuechenxiao Cao, Yutong Li, Bing Liu, Jiaming Liang, Tinghe Zhang, Xiaoqi Sheng, Hongmin Cai

rag

arXiv AI

Jun 15

CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context

arXiv:2606. 13713v1 Announce Type: cross Abstract: Predicting cellular transcriptional responses to genetic perturbations is a central problem in single-cell biology, especially in the zero-shot setting where the perturbed gene or gene combination is unseen during training.

By Wei Zhang, Xun Jiang, Yuesi Xi, Ming Tang

multimodal benchmarks

arXiv AI

3d ago

CellMSA: Context Modeling for Single-Cell Representation Learning

CellMSA introduces a novel single‑cell representation learning framework that leverages a multiple‑sequence‑alignment‑inspired context model. For each target cell, it retrieves relevant cells across batches and related cell types, summarizing cross‑cell patterns into a context‑dependent gene‑pair representation that is fed into a pair‑aware encoder. Pretraining on a massive human single‑cell corpus (≈109 million cells) and subsequent benchmarks demonstrate consistent performance gains over existing methods.

By Suyuan Zhao, Minghao Liu, Yizhen Luo, Zaiqing Nie

efficiency benchmarks safety

arXiv Machine Learning

Jun 2

Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference

arXiv:2606. 00685v1 Announce Type: new Abstract: Gene regulatory networks (GRNs) capture transcription factor-target interactions and are central to understanding cell-state regulation and disease.

By Tianyang Xu, Tianci Liu, Niraj Rayamajhi, Ryan Patrick, Kranthi Varala, Ying Li, Jing Gao

llms multimodal

arXiv Machine Learning

Jun 17

Beyond Independent Genes: Learning Module-Inductive Representations for Single-Cell Gene Perturbation Prediction

arXiv:2602. 04901v2 Announce Type: replace-cross Abstract: Predicting transcriptional responses to genetic perturbations is a central problem in functional genomics.

By Jiafa Ruan, Ruijie Quan, Liyang Xu, Zongxin Yang, Yi Yang

diffusion benchmarks safety

arXiv Machine Learning

Jun 29

PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction

arXiv:2606. 27752v1 Announce Type: new Abstract: Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions.

By Dongxia Wu, Mingyu Li, Yuhui Zhang, Anurendra Kumar, Emma Lundberg, Serena Yeung-Levy, Emily B. Fox

reinforcement-learning benchmarks safety

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Related stories

DeMixPert: Decomposed Response Modeling with Gaussian Mixtures for OOD Single-Cell Perturbation Prediction

CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context

CellMSA: Context Modeling for Single-Cell Representation Learning

Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference

Beyond Independent Genes: Learning Module-Inductive Representations for Single-Cell Gene Perturbation Prediction

PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction