ResearcherThe Ohio State University Wexner Medical CenterActive in the last 2 years
Abdul Akbar
Matched on ORCID iD 0009-0004-2352-6360; OpenAlex author ID A5062903400. Sources: OpenAlex.
Their work
- Papers
- 4 (first author 2, last author 0)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 8
2026: 4 papers
Papers 4
- Gene-structured histology for deriving and predicting pancreatic cancer molecular subtypes First author DOI 10.1038/s41392-026-03051-2↗
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning Co-author DOI 10.21203/rs.3.rs-8545050/v1↗
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning Co-author DOI 10.64898/2026.01.06.26343539↗
- Gene–Morphology Alignment via Graph-Constrained Latent Modeling for Molecular Subtype Prediction from Histopathology in Pancreatic Cancer First author DOI 10.64898/2026.03.05.26347711↗
Closest collaborators
The 8 strongest of 8 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Muhammad Khalid Khan Niazi · 4 shared papers, 2026 · strength 1.53
- Paper Gene-structured histology for deriving and predicting pancreatic cancer molecular subtypes DOI 10.1038/s41392-026-03051-2↗
- Paper Gene–Morphology Alignment via Graph-Constrained Latent Modeling for Molecular Subtype Prediction from Histopathology in… DOI 10.64898/2026.03.05.26347711↗
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.21203/rs.3.rs-8545050/v1↗
- Alejandro Leyva · 2 shared papers, 2026 · strength 1.00
- Upender Manne Manne · 2 shared papers, 2026 · strength 0.53
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.21203/rs.3.rs-8545050/v1↗
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.64898/2026.01.06.26343539↗
- Ashwini Kumar Esnakula · 2 shared papers, 2026 · strength 0.53
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.21203/rs.3.rs-8545050/v1↗
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.64898/2026.01.06.26343539↗
- Ashish Manne · 2 shared papers, 2026 · strength 0.53
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.21203/rs.3.rs-8545050/v1↗
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.64898/2026.01.06.26343539↗
- Susan Tsai · 2 shared papers, 2026 · strength 0.53
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.21203/rs.3.rs-8545050/v1↗
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.64898/2026.01.06.26343539↗
- Anne M. Noonan · 2 shared papers, 2026 · strength 0.53
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.21203/rs.3.rs-8545050/v1↗
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.64898/2026.01.06.26343539↗
- Vaibhav Sahai · 2 shared papers, 2026 · strength 0.53
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.21203/rs.3.rs-8545050/v1↗
- Paper Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning DOI 10.64898/2026.01.06.26343539↗
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.