54 citations · 163 across the 10 of their papers we have counts for
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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
Sparse Task Vector Mixup with Hypernetworks for Efficient Knowledge Transfer in Whole-Slide Image Prognosis
Pei Liu, Xiangxiang Zeng, Tengfei Ma +3
Whole-Slide Images (WSIs) are widely used for estimating the prognosis of cancer patients. Current studies generally follow a cancer-specific learning paradigm. However, the availa…