5 papers
TCellAlign: Cross-study T-cell Populations Alignment with Nomenclature-Guided Multi-Agent Workflow
Pengyu Xie, Rongjia Zhou, Zhilin Ou +5
Cell type standardization plays a central role in integrating biological knowledge across single-cell studies. While standardized resources (e.g., Cell Ontology, Nomenclature Frame…
Learning Optimal Multimodal Information Bottleneck Representations
Qilong Wu, Yiyang Shao, Jun Wang +1
Leveraging high-quality joint representations from multimodal data can greatly enhance model performance in various machine-learning based applications. Recent multimodal learning…
MEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with Spatial Transcriptomics
Kaichen Xu, Qilong Wu, Yan Lu +5
The detection of anomalous tissue regions (ATRs) within affected tissues is crucial in clinical diagnosis and pathological studies. Conventional automated ATR detection methods, pr…
Dual Advancement of Representation Learning and Clustering for Sparse and Noisy Images
Wenlin Li, Yucheng Xu, Xiaoqing Zheng +3
Sparse and noisy images (SNIs), like those in spatial gene expression data, pose significant challenges for effective representation learning and clustering, which are essential fo…
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond
Kaichen Xu, Yueyang Ding, Suyang Hou +4
Fined-grained anomalous cell detection from affected tissues is critical for clinical diagnosis and pathological research. Single-cell sequencing data provide unprecedented opportu…