Author

E. A. Burlingame

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 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Research Focus:clustering· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:imaging· 2 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

C. Corless· 3 shared G. Thibault· 3 shared J. W. Gray· 3 shared Y. H. Chang· 3 shared A. Blucher· 1 shared A. Kolodzie· 1 shared A. L. Creason· 1 shared A. R. Guimaraes· 1 shared

Activity by Year

2019-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersCancer Biology

C. Corless

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3 papers

Recent·Most cited

Transfer Learning for Inference of Metastatic Origin from Whole Slide Histology

G. Schau, H. Ghani, E. A. Burlingame, G. Thibault, J. W. Gray, C. Corless, Y. H. Chang

This paper uses transfer learning to predict metastatic origin from H&E-stained tumor images, achieving higher accuracy than models trained on primary or metastatic tumors alone.

bioRxiv · Apr 21, 2021 · PreprintPathology

An Omic and Multidimensional Spatial Atlas from Serial Biopsies of an Evolving Metastatic Breast Cancer

B. E. Johnson, A. L. Creason, J. M. Stommel, J. M. Keck, S. Parmar, C. B. Betts, A. Blucher, C. Boniface, E. Bucher, E. A. Burlingame, T. Camp, K. Chin, J. Eng, J. Estabrook, H. S. Feiler, , J. W. Gray

This paper presents an OMS Atlas from serial biopsies of a metastatic breast cancer patient, linking clinical data to multi-omic and spatial profiles to study therapy-associated heterogeneity and resistance mechanisms.

bioRxiv · Dec 3, 2020Cancer Biology

Predicting Primary Site of Secondary Liver Cancer with a Neural Estimator of Metastatic Origin (NEMO)

G. F. Schau, E. A. Burlingame, G. Thibault, T. Anekpuritanang, Y. Wang, J. W. Gray, C. Corless, Y. H. Chang

The paper introduces a deep learning model to predict the metastatic origin of secondary liver cancer from H&E stained tissue sections, achieving 90.2% accuracy comparable to expert pathologists.

bioRxiv · Jul 2, 2019Cancer Biology