Jing Su

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Assistant Professor

Indiana University School of Medicine

United States

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Jing Su is an Assistant Professor at Indiana University School of Medicine, USA. His research focuses on areas such as single-cell spatial transcriptomics, artificial intelligence in bioinformatics, and health disparities. Recent publications include studies on personalized real-time haemodynamic targets and decision support systems for chronic kidney disease.

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Revealing Health Trajectories of Chronic Kidney Disease for Precision Medicine

Open Date: 2022-08-01

Close Date: 2026-05-31

Jing Su

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Articles (16)

SpaIM: single-cell spatial transcriptomics imputation via style transfer

HECLIP: histology-enhanced contrastive learning for imputation of transcriptomics profiles

The HM-TARGET personalised real-time haemodynamic targets in critical care

Risk of Urinary Tract Infections with Sodium-Glucose Transport Protein-2 Inhibitors in Subpopulations with Abnormal Genitourinary Pathology

Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics

TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease

Health disparities in the risk of severe acidosis: real-world evidence from the All of Us cohort

SiGra: single-cell spatial elucidation through an image-augmented graph transformer

Sample average treatment effect on the treated (SATT) analysis using counterfactual explanation identifies BMT and SARS-CoV-2 vaccination as protective risk factors associated with COVID-19 severity and survival in patients with multiple myeloma

SMGR: a joint statistical method for integrative analysis of single-cell multi-omics data

Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data

Pharmacogenomics of Hypertension in CKD: The CKD-PGX Study

Reply to K. Takada et al

Robust estimation of heterogeneous treatment effects: an algorithm-based approach

scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics

scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

Collaborators (11)

Yingjie Chen

Baijian Yang

Noha Sharafeldin

AMIT MITRA

Lance Miller

Honglang Wang

Wen-jie Chen

Feifan Liu

Qianqian Song

Tieying Hou

Yi Zhao

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