arXiv AI

Scaling an Autoregressive Transformer for Single-Cell Generation

arXiv:2608. 02961v1 Announce Type: cross Abstract: We study a self-supervised generation task for single-cell gene expression vectors: given a set of vectors from a cell type, we aim to generate additional gene expression vectors of that cell type.

arXiv Machine Learning

Jun 3

Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models

arXiv:2511. 02986v2 Announce Type: replace-cross Abstract: Computational modeling of single-cell gene expression is crucial for understanding cellular processes, but generating realistic expression profiles remains a major challenge.

By Giovanni Palla, Sudarshan Babu, Payam Dibaeinia, James D. Pearce, Donghui Li, Aly A. Khan, Theofanis Karaletsos, Jakub M. Tomczak

llms diffusion

arXiv Machine Learning

Jun 9

Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics

arXiv:2602. 15253v2 Announce Type: replace Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored.

By Ihor Kendiukhov

llms nlp

arXiv Machine Learning

Aug 4

Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views

arXiv:2608. 00985v1 Announce Type: new Abstract: The rapid growth of single-cell transcriptomic data has enabled the development of foundation models pretrained primarily by reconstructing masked expression values.

By Jiaqi Xiong, Yuntao hu, Yu Zheng, Yifei Shi, Xinyue Guo, Jiaxin Qi

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

arXiv AI

Jul 23

Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min-Max Selection

arXiv:2607. 19426v1 Announce Type: cross Abstract: Single-cell datasets are increasingly costly to store, audit, and reuse for model training.

By Yaodi Luo, Peize He, Bowen Han, Lingbei Mengg

efficiency

arXiv AI

Aug 5

Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min--Max Selection

arXiv:2607. 19426v2 Announce Type: replace-cross Abstract: Large single-cell datasets are expensive to store, curate, and repeatedly reuse for model training.

By Yaodi Luo, Peize He, Lingbei Meng, Bowen Han, Zheng Lu, Jianqing Zhu, Lian Zhang

rag efficiency benchmarks

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

Jun 12

OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction

arXiv:2606. 12838v1 Announce Type: cross Abstract: Predicting single-cell transcriptional responses to genetic, chemical and cytokine perturbations is a fundamental challenge in computational biology and AI Virtual Cell (AIVC) modeling, with direct implications for drug discovery and the elucidation of gene regulatory networks.

By Danning Jiang, Zheming An, Yalong Zhao, Lipeng Lai

llms rag benchmarks

arXiv Machine Learning

Jun 9

Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis

arXiv:2606. 09558v1 Announce Type: cross Abstract: Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supervised learning on millions of cells.

By Mikele Milia, Louis Fabrice Tshimanga, Henning Mueller, Manfredo Atzori, Barbara Di Camillo

llms rag 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 AI

Jun 2

GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics

arXiv:2606. 02424v1 Announce Type: cross Abstract: Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images and cell locations, reducing the need for costly single-cell ST measurements.

By Kaito Shiku, Ahtisham Fazeel Abbasi, Ryoma Bise, Yuichiro Iwashita, Kazuya Nishimura, Andreas Dengel, Muhammad Nabeel Asim

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

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