Xubin Zheng

The paper introduces SPADE, a framework that detects spatially aberrant cells in spatial transcriptomics data using variational autoencoders and conformal prediction.

DIME integrates spatial multi-omics data from unaligned serial sections using deep learning, achieving robust clustering and domain identification in human tissue datasets.

This paper introduces scCaT, a deep learning framework combining capsulating architecture and Transformer for sepsis diagnosis using single-cell RNA sequencing data and transferring to bulk RNA data, achieving high accuracy and explainability.

The paper identifies circular RNA competing endogenous gene pairs (cceGPs) as predictive indicators for serous ovarian cancer using a novel method and validates their predictive capacity in independent cohorts.