3 papers
q-bio.GN2026
Panoramic modelling of single-cell transcriptomes at scale with CellNet
Xiaoqing Lian, Pei Liu, Jiahui Tan +5
Comprehensively characterizing cellular diversity is critical for understanding tissue development, identifying disease-associated populations and investigating gene programmes lin…
cs.AI2026
Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity
Xiaoqing Lian, Pei Liu, Jiahui Tan +3
Single-cell RNA sequencing (scRNA-seq) enables cell-level analysis of biological heterogeneity, but learning effective representations from high-dimensional, unordered gene express…
cs.CV2026
scMIR: a vision-language foundation model for single-cell light microscopy image representation
Yifan Shang, Jiahui Tan, Xiangxiang Zeng +1
Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity pose challenges to high-throughp…