ResearcherJohns Hopkins UniversityActive in the last 2 years
Pei-Hsun Wu
Matched on ORCID iD 0000-0002-7371-2960; OpenAlex author ID A5063226324, A5128143028. Sources: OpenAlex.
Their work
- Papers
- 4 (first author 1, last author 2)
- Trials
- 0
- Grants
- 0
- Labs led
- 1
- Collaborators
- 17
2017: 1 paper · 2018: none · 2019: none · 2020: none · 2021: none · 2022: 1 paper · 2023: none · 2024: 1 paper · 2025: none · 2026: 1 paper
Papers 4
- Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer Co-author DOI 10.64898/2026.03.03.709150↗
- Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings. First author DOI 10.1126/sciadv.ado5103↗
- CODA: quantitative 3D reconstruction of large tissues at cellular resolution Last author DOI 10.1038/s41592-022-01650-9↗
- Computational discovery of tissue morphology biomarkers in very long-term survivors with pancreatic ductal adenocarcinoma Last author DOI 10.1101/207969↗
Closest collaborators
The 17 strongest of 17 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Laura DeLong Wood · 4 shared papers, 2017–2026 · strength 0.65
- Paper Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic… DOI 10.1126/sciadv.ado5103↗
- Paper Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in … DOI 10.64898/2026.03.03.709150↗
- Paper CODA: quantitative 3D reconstruction of large tissues at cellular resolution DOI 10.1038/s41592-022-01650-9↗
- Denis Wirtz · 4 shared papers, 2017–2026 · strength 0.65
- Paper Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic… DOI 10.1126/sciadv.ado5103↗
- Paper Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in … DOI 10.64898/2026.03.03.709150↗
- Paper CODA: quantitative 3D reconstruction of large tissues at cellular resolution DOI 10.1038/s41592-022-01650-9↗
- Ashley L. Kiemen · 3 shared papers, 2022–2026 · strength 0.57
- Paper Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic… DOI 10.1126/sciadv.ado5103↗
- Paper Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in … DOI 10.64898/2026.03.03.709150↗
- Paper CODA: quantitative 3D reconstruction of large tissues at cellular resolution DOI 10.1038/s41592-022-01650-9↗
- Ralph H. Hruban MD · 3 shared papers, 2017–2024 · strength 0.43
- Paper Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic… DOI 10.1126/sciadv.ado5103↗
- Paper CODA: quantitative 3D reconstruction of large tissues at cellular resolution DOI 10.1038/s41592-022-01650-9↗
- Paper Computational discovery of tissue morphology biomarkers in very long-term survivors with pancreatic ductal adenocarcino… DOI 10.1101/207969↗
- Toby C. Cornish · 2 shared papers, 2022–2024 · strength 0.35
- David Zwicker · 1 shared paper, 2024 · strength 0.23
- Dimitrios N. Sidiropoulos · 1 shared paper, 2026 · strength 0.22
- Elana Judith Fertig · 1 shared paper, 2026 · strength 0.22
- Paola A. Guerrero · 1 shared paper, 2026 · strength 0.22
- Luciane Tsukamoto Kagohara · 1 shared paper, 2026 · strength 0.22
- André Forjaz · 1 shared paper, 2026 · strength 0.22
- Vincent Bernard Pagan · 1 shared paper, 2026 · strength 0.22
- Won Jin Ho · 1 shared paper, 2026 · strength 0.22
- Seung‐Mo Hong · 1 shared paper, 2022 · strength 0.12
- Jacob S. Sarnecki · 1 shared paper, 2017 · strength 0.08
- Christopher Lee Wolfgang · 1 shared paper, 2017 · strength 0.08
- Anirban Maitra · 1 shared paper, 2017 · strength 0.08
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.