arXiv Machine Learning June 30, 2026 By Jun Tang, Pengwei Hu, Sicong Gao, Jie Guo, Lun Hu, Xin Luo

scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering

Read the original on arXiv Machine Learning →

arXiv:2606. 28459v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) clustering is essential for identifying cell types, but high dimensionality, sparsity, dropout, and technical noise hinder robust expression representation and cell graph construction.

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

efficiency

arXiv AI

Jun 18

scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering

arXiv:2606. 18672v1 Announce Type: cross Abstract: Single-cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of cell types and advancing the understanding of cellular heterogeneity.

By Jinke Wu, Yifan Wang, Siyu Yi, Caiyang Yu, Ziyue Qiao, Nan Yin, Jiancheng Lv, Wei Ju

llms efficiency benchmarks

arXiv AI

Jun 12

scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing

arXiv:2606. 13007v1 Announce Type: cross Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity.

By Ping Xu, Pengjiang Li, Tian Du, Zaitian Wang, Jiawei Gu, Ziyue Qiao, Pengfei Wang, Yuanchun Zhou

llms rag diffusion multimodal benchmarks safety

Hugging Face Trending Papers

Aug 6

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individual genes. Here we present BioM-JEPA, a joint-embedding predictive architecture that instead predicts aggregate representations of graph-connected gene blocks defined by protein-association and corpus-derived coexpression evidence.

rag fine-tuning efficiency

arXiv Machine Learning

Aug 7

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

arXiv:2608. 05928v1 Announce Type: new Abstract: Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individual genes.

By Yuhao Wang, Zelin Zang, Yuxuan Liu, Zhen Lei, Stan Z. Li

rag fine-tuning efficiency

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 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

Related stories

scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering

scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views

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