Author

J. O. Johnson

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 2 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Organism:human· 1 Assay:imaging· 1 Organism:mouse· 1

Frequent Co-Authors

M. A. Abdalah· 2 shared A. F. Tassielli· 1 shared A. M. E. Ojwang'· 1 shared A. Ortiz· 1 shared B. A. Centeno· 1 shared B. Sarcar· 1 shared B. V. Jardim Perassi· 1 shared D. Jeong· 1 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersCancer Biology

M. A. Abdalah

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3 papers

Recent·Most cited

Histology-guided mathematical model of tumor oxygenation: sensitivity analysis of physical and computational parameters

A. M. E. Ojwang', S. Bazargan, J. O. Johnson, S. Pilon-Thomas, K. A. Rejniak

The paper develops a histology-guided tumor oxygenation model and finds it insensitive to most parameters but sensitive to large perturbations and boundary conditions.

bioRxiv · Mar 5, 2024 · PreprintSystems Biology

Establishing a Living Biobank of Patient-Derived Organoids of Intraductal Papillary Mucinous Neoplasms of the Pancreas

F. Beato, D. Reveron, K. B. Dezsi, A. Ortiz, J. O. Johnson, D.-T. Chen, K. Ali, S. J. Yoder, D. Jeong, M. Malafa, P. Hodul, K. Jiang, B. A. Centeno, M. A. Abdalah, J. A. Balasi, , J. B. Fleming

This study establishes a living biobank of patient-derived organoids from intraductal papillary mucinous neoplasms to study their progression and identify biomarkers for early detection and prevention.

Laboratory Investigation · Feb 1, 2021Peer-reviewedCancer Biology

Targeting hypoxic habitats with hypoxia pro-drug evofosfamide in preclinical models of sarcoma

B. V. Jardim Perassi, W. Mu, S. Huang, M. R. Tomaszewski, J. Poleszczuk, M. A. Abdalah, M. M. Budzevich, W. Dominguez-Viqueira, D. R. Reed, M. M. Bui, J. O. Johnson, G. V. Martinez, R. J. Gillies

This paper develops deep-learning models to identify hypoxic tumor regions using MRI and histology, showing their effectiveness in predicting hypoxia and guiding TH-302 therapy response in sarcoma models.

bioRxiv · Oct 6, 2020 · PreprintCancer Biology