Author

A. Srisuwananukorn

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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Approach:deep learning· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Assay:clinical cohort / trial· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:imaging· 1

Frequent Co-Authors

A. T. Pearson· 2 shared J. Dolezal· 2 shared S. Kochanny· 2 shared A. Woodard· 1 shared C. Loeffler· 1 shared C. M. Perou· 1 shared C. Trautwein· 1 shared D. Huo· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersCancer Biology

A. T. Pearson

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2 papers

Recent·Most cited

Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of Recurrence

F. M. Howard, J. Dolezal, S. Kochanny, G. Khramtsova, J. Vickery, A. Srisuwananukorn, A. Woodard, N. Chen, R. Nanda, C. M. Perou, O. I. Olopade, D. Huo, A. T. Pearson

This paper develops a deep learning model that predicts breast cancer recurrence using digital histology and clinical factors, outperforming a clinical nomogram and identifying patients who may avoid genomic testing.

bioRxiv · Jul 7, 2022 · PreprintCancer Biology

Deep learning detects virus presence in cancer histology

J. N. Kather, J. Schulte, H. I. Grabsch, C. Loeffler, H. S. Muti, J. Dolezal, A. Srisuwananukorn, N. Agrawal, S. Kochanny, S. von Stillfried, P. Boor, T. Yoshikawa, D. Jaeger, C. Trautwein, P. Bankhead, , A. T. Pearson

This paper uses deep learning to detect the presence of HPV and EBV in cancer histology images, achieving high accuracy in predicting virus-associated tumors.

bioRxiv · Jul 5, 2019 · PreprintCancer Biology