Pancreatic Cancer Research

ResearcherSeoul National UniversityActive in the last 2 years

Taesung Park

ORCID-verified (high confidence, 0.95), identity_conf@1As of 8 November 2026

Matched on ORCID iD 0000-0002-8294-590X; OpenAlex author ID A5100643662. Sources: OpenAlex.

Their work

Papers
22 (first author 2, last author 4)
Trials
0
Grants
0
Labs led
1
Collaborators
66
Papers, trials and grants by year

2014: 1 paper · 2015: 1 paper · 2016: 1 paper · 2017: 1 paper · 2018: 1 paper · 2019: none · 2020: 2 papers · 2021: 4 papers · 2022: 1 paper · 2023: 9 papers · 2024: none · 2025: 1 paper

Papers 22

  1. Machine learning models for pancreatic cancer diagnosis based on microbiome markers from serum extracellular vesicles. Last author DOI 10.1038/s41598-025-94183-y↗
  2. Features of T1 pancreatic cancer and validation of the eighth edition AJCC staging system definition using a Korean-Japanese joint cohort and the SEER database. Co-author DOI 10.1002/jhbp.1316↗
  3. Multi-biomarker panel prediction model for diagnosis of pancreatic cancer. Co-author DOI 10.1002/jhbp.986↗
  4. Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes. Co-author DOI 10.1038/s43018-022-00479-7↗
  5. Author Correction: Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtype… Co-author DOI 10.1038/s43018-023-00581-4↗
  6. Whole exome sequencing and RNA sequencing analyses of PDAC samples Co-author DOI 10.21203/rs.3.pex-2062/v1↗
  7. Mass spectrometry-based proteomic analysis of PDAC samples Co-author DOI 10.21203/rs.3.pex-2063/v1↗
  8. Bioinformatics analysis of PDAC subtypes Co-author DOI 10.21203/rs.3.pex-2064/v1↗
  9. Cell-based assays for potential prognostic biomarkers in PDAC Co-author DOI 10.21203/rs.3.pex-2065/v1↗
  10. Assays for orthotopic PDAC mouse models Co-author DOI 10.21203/rs.3.pex-2066/v1↗
  11. Comparison of survival prediction models for pancreatic cancer: Cox model versus machine learning models. First author DOI 10.5808/gi.22036↗
  12. Microbiome Markers of Pancreatic Cancer Based on Bacteria-Derived Extracellular Vesicles Acquired from Blood Samples: A Retrospective Propensity Score Matching… Co-author DOI 10.3390/biology10030219↗
  13. Diagnostic model for pancreatic cancer using a multi-biomarker panel. Co-author DOI 10.4174/astr.2021.100.3.144↗
  14. CORRIGENDUM: Addition of data source: Diagnostic model for pancreatic cancer using a multi-biomarker panel. Co-author DOI 10.4174/astr.2021.100.4.252↗
  15. Development and External Validation of Survival Prediction Model for Pancreatic Cancer Using Two Nationwide Databases: Surveillance, Epidemiology and End Resul… Last author DOI 10.5009/gnl20306↗
  16. Is the New T1 Category as Defined in the Eighth Edition of the AJCC Pancreatic Cancer Staging System an Improvement? First author DOI 10.1007/s11605-019-04464-x↗
  17. Multi-Biomarker Panel Prediction Model for Diagnosis of Pancreatic Cancer: a retrospective and multi-center cohort study Co-author DOI 10.21203/rs.3.rs-76136/v1↗
  18. Correction: Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel. Co-author DOI 10.18632/oncotarget.24175↗
  19. Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel. Co-author DOI 10.18632/oncotarget.21861↗
  20. Molecular subtypes of pancreatic cancer based on miRNA expression profiles have independent prognostic value. Co-author DOI 10.1111/jgh.13253↗
  21. Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer. Last author DOI 10.1186/1471-2164-16-s9-s4↗
  22. Identifying molecular subtypes related to clinicopathologic factors in pancreatic cancer. Last author DOI 10.1186/1475-925x-13-s2-s5↗

Closest collaborators

The 25 strongest of 66 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

  1. Wooil Kwon · 19 shared papers, 2016–2025 · strength 2.07
  2. Jin‐Young Jang · 16 shared papers, 2014–2025 · strength 1.74
  3. Hongbeom Kim · 14 shared papers, 2020–2023 · strength 1.53
  4. Seungyeoun Lee · 14 shared papers, 2014–2025 · strength 1.51
  5. Min‐Sik Kim · 7 shared papers, 2023 · strength 0.83
  6. Sangyeop Hyun · 7 shared papers, 2023 · strength 0.83
  7. Sung Hee Baek · 7 shared papers, 2023 · strength 0.83
  8. Keehoon Jung · 7 shared papers, 2023 · strength 0.83
  9. Sang‐Won Lee · 7 shared papers, 2023 · strength 0.83
  10. Do Young Hyeon · 7 shared papers, 2023 · strength 0.83
  11. Ja‐Lok Ku · 7 shared papers, 2023 · strength 0.83
  12. Duk Ki Kim · 7 shared papers, 2023 · strength 0.83
  13. Gibeom Kim · 7 shared papers, 2023 · strength 0.83
  14. Daechan Park · 7 shared papers, 2023 · strength 0.83
  15. Jin Seok Heo · 8 shared papers, 2017–2023 · strength 0.72
  16. Jin‐Young Jang · 6 shared papers, 2016–2023 · strength 0.65
  17. Chang Moo Kang · 7 shared papers, 2017–2023 · strength 0.63
  18. Junghyun Namkung · 8 shared papers, 2015–2023 · strength 0.61
  19. Song Cheol Kim · 6 shared papers, 2018–2023 · strength 0.58
  20. Joo Kyung Park · 6 shared papers, 2017–2023 · strength 0.51
  21. Yoonhyeong Byun · 5 shared papers, 2020–2023 · strength 0.50
  22. Jae Seung Kang · 5 shared papers, 2020–2023 · strength 0.49
  23. Woo Jin Lee · 5 shared papers, 2018–2023 · strength 0.46
  24. Joon Oh Park · 5 shared papers, 2018–2023 · strength 0.46
  25. In Woong Han · 4 shared papers, 2020–2023 · strength 0.42

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.