Jean Yang

Professor

University of Sydney

Australia

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About

Jean Yang is a Professor at the University of Sydney, Australia. His research primarily focuses on single-cell resolution gene expression, deep learning applications in histology, and metabolic pathways in proliferating cells. He has contributed to significant publications, including works on interpretable microbial phenotype analysis and drug-diet interaction clustering methods.

Articles (29)

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2025

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2023

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2023

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2022

Collaborators (21)

Freda H. Passam

AUSTRALIA

Jean Yang

Research Interests

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About

Articles (29)

Benchmarking the translational potential of spatial gene expression prediction from histology

Spatial gene expression at single-cell resolution from histology using deep learning with GHIST

A message passing framework for precise cell state identification with scClassify2

Pathway metabolite ratios reveal distinctive glutamine metabolism in a subset of proliferating cells

Interpretable Differential Abundance Signature (iDAS)

Vascular Cytokines and Atherosclerosis: Differential Serum Levels of TRAIL, IL-18, and OPG in Obstructive Coronary Artery Disease

Imputation of plasma lipid species to facilitate integration of lipidomic datasets

Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention

Rapid benefits in older age from transition to whole food diet regardless of protein source or fat to carbohydrate ratio: Arandomised control trial

Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas

The Efficacy of Risk Factor Modification Compared to NAD+ Repletion in Diastolic Heart Failure

Construction and optimization of multi-platform precision pathways for precision medicine

BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

IMPACT: interpretable microbial phenotype analysis via microbial characteristic traits

Scope+: an open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels

eNODAL: an experimentally guided nutriomics data clustering method to unravel complex drug–diet interactions

Histone marks identify novel transcription factors that parse CAR-T subset-of-origin, clinical potential and expansion

Deep multimodal graph-based network for survival prediction from highly multiplexed images and patient variables

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

Endoplasmic reticulum protein 5 attenuates platelet endoplasmic reticulum stress and secretion in a mouse model

scSTAR reveals hidden heterogeneity with a real-virtual cell pair structure across conditions in single-cell RNA sequencing data

NEMoE: a nutrition aware regularized mixture of experts model to identify heterogeneous diet-microbiome-host health interactions

Thinking process templates for constructing data stories with SCDNEY

Thinking process templates for constructing data stories with SCDNEY

Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data

Collaborators (21)

Freda H. Passam

Jonathan Shaw

Mark Larance

John O'Sullivan

Yue Cao

KongFatt Wong-Lin

Hani Jieun Kim

Maggie Kalev-Zylinska

Michael Patrick Gray

Pengyi Yang

Jennie Hui

Philip Hogg

Ellis Patrick

Dianna Magliano

Yunwei Zhang

Jian Tan

Samuel Muller

jinman kim

John Beilby

Ye Zheng

Shila Ghazanfar

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