ResearcherUniversity of Nebraska–LincolnActive in the last 2 years

Chi Zhang

ORCID-verified (high confidence, 0.95), identity_conf@1As of 7 November 2026

Matched on ORCID iD 0000-0002-0150-6500; OpenAlex author ID A5089300674. Sources: OpenAlex.

Their work

Papers: 2 (first author 0, last author 1)
Trials: 0
Grants: 0
Labs led: 0
Collaborators: 9

Papers, trials and grants by year

2023: 1 paper · 2024: 1 paper

Papers 2

radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. Last author DOI 10.1038/s41598-024-62741-5 ↗
3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for P… Co-author DOI 10.3390/cancers15235496 ↗

Closest collaborators

The 9 strongest of 9 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

Michael Anthony Hollingsworth · 2 shared papers, 2023–2024 · strength 0.37
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5 ↗
Hongfeng Yu · 2 shared papers, 2023–2024 · strength 0.37
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5 ↗
Dandan Zheng · 2 shared papers, 2023–2024 · strength 0.37
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5 ↗
Michael J. Baine · 2 shared papers, 2023–2024 · strength 0.37
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5 ↗
Yu Shi · 1 shared paper, 2023 · strength 0.19
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗
Kiersten Preuss · 1 shared paper, 2024 · strength 0.18
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5 ↗
Yu Shi · 1 shared paper, 2024 · strength 0.18
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5 ↗
Paul M. Grandgenett · 1 shared paper, 2024 · strength 0.18
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5 ↗
Jeffrey Wong · 1 shared paper, 2024 · strength 0.18
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5 ↗

How to reach this person

A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.