Yuanning Zheng

Digital pathological classification of peripheral neuroblastic tumors across multi-continent cohorts

The paper develops NB-PathAssist, a computational framework for automated classification of peripheral neuroblastic tumors using whole-slide histology images, achieving high accuracy in prognostic grouping.

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight

The paper introduces Moonlight2, an updated framework that integrates transcriptomic and epigenomic data to identify cancer driver genes, finding 33, 190, and 263 epigenetically driven genes in three cancer subtypes.

Response to anti-angiogenic therapy is affected by AIMP protein family activity in glioblastoma and lower-grade gliomas

The paper investigates the role of AIMP proteins in glioma angiogenesis and their predictive value for anti-angiogenic therapy response using multi-omics and single-cell data.

Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients.

This study uses deep learning to predict YAP1/TEAD activity from histology slides, linking histological patterns to disease aggressiveness in cancer patients.

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

This paper introduces SEQUOIA, a transformer model that predicts cancer transcriptomes from histology images, achieving high accuracy in predicting gene expression and recurrence risk in breast cancer.

EpiMix: an integrative tool for epigenomic subtyping using DNA methylation

EpiMix identifies differential DNA methylation in large cohorts with higher sensitivity than existing methods, revealing novel epigenetic mechanisms in childhood food allergy and lung cancer.

RNA-to-image multi-cancer synthesis using cascaded diffusion models

This paper introduces RNA-Cascaded-Diffusion-Model to generate synthetic whole-slide image tiles from RNA-Seq data, improving machine learning model performance in low-data settings.