Author

M. U. Oner

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

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:clustering· 1 Research Focus:spatial domain detection· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

H. K. Lee· 2 shared W.-K. Sung· 2 shared A. Jacobsen Skanderup· 1 shared A. James· 1 shared A. N. Kaya· 1 shared A. Takano· 1 shared C. E. C. Xi· 1 shared C. F. Wong· 1 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersCancer Biology

H. K. Lee

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2 papers

Recent·Most cited

An AI-assisted Tool For Efficient Prostate Cancer Diagnosis

M. U. Oner, M. Y. Ng, D. M. Giron, C. E. C. Xi, L. A. Y. Xiang, M. Singh, W. Yu, W.-K. Sung, C. F. Wong, H. K. Lee

This paper develops a deep learning pipeline to detect malignant glands in prostate cancer core needle biopsies, achieving high accuracy and providing heatmaps for pathologists.

bioRxiv · Feb 6, 2022 · PreprintCancer Biology

Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study

M. U. Oner, J. Chen, E. Revkov, A. James, S. Y. Heng, A. N. Kaya, J. J. S. Alvarez, A. Takano, X. M. Cheng, T. K. H. Lim, D. S. W. Tan, W. Zhai, A. Jacobsen Skanderup, W.-K. Sung, H. K. Lee

This paper develops a deep multiple instance learning model to predict tumor purity from histopathology slides, showing spatial variation and improving sample selection for genomic analysis.

bioRxiv · Jul 8, 2021Cancer Biology