arXiv AI August 3, 2026 By Yu Song, Hao Sun, Ikuko Nishikawa, Yen-Wei Chen

Improving scDiffusion with Sparsity-Biased Classifier-Free Guidance

arXiv:2607. 29043v1 Announce Type: cross Abstract: Single-cell RNA sequencing (scRNA-seq) has become an essential tool in modern cellular biology, and generating accurate synthetic scRNA-seq data is becoming increasingly important.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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

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 AI

Jun 16

BRIDGE: Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks

arXiv:2606. 14734v1 Announce Type: cross Abstract: Motivation: Gene regulatory network inference from single-cell RNA sequencing (scRNA-seq) data is important for uncovering cell-state-specific transcriptional programs.

By Ziyang Dong, Shanwen Tan, Hengchuang Yin, Wei Liu, Yifan Wang, Siyu Yi, Jiancheng Lv, Wei Ju

benchmarks

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

Jul 28

SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing

arXiv:2607. 23821v1 Announce Type: new Abstract: Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology.

By Shuyu Chen, Chen Zhu, Ye Zhang, Yang Li, Qiqi Xie, Haohan Wang

agents benchmarks safety

arXiv Machine Learning

Jun 9

scCBGM: Interpretable Single-Cell Counterfactual Editing

arXiv:2606. 07760v1 Announce Type: new Abstract: Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design.

By Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer, Doron Haviv, Tommaso Biancalani, Kyunghyun Cho, Gabriele Scalia, A\"icha BenTaieb, Hector Corrada Bravo

diffusion benchmarks

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

Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views

BRIDGE: Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks

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

SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing

scCBGM: Interpretable Single-Cell Counterfactual Editing