Author

G. Khramtsova

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 Research Focus:drug response / drug discovery· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:clinical cohort / trial· 1 Organism:human· 1 Assay:imaging· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

A. T. Pearson· 2 shared F. M. Howard· 2 shared J. Dolezal· 2 shared S. Kochanny· 2 shared A. Srisuwananukorn· 1 shared A. Woodard· 1 shared B. Feiger· 1 shared C. Fan· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersCancer Biology

A. T. Pearson

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Author profile →

2 papers

Recent·Most cited

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features

F. M. Howard, H. Hieromnimon, S. Ramesh, J. Dolezal, S. Kochanny, Q. Zhang, B. Feiger, J. Peterson, C. Fan, C. Perou, J. M. Vickery, M. Sullivan, K. Cole, G. Khramtsova, A. T. Pearson

This paper introduces HistoXGAN, a GAN that reconstructs cancer histology from latent features, showing it retains tumor grade, histologic subtype, and gene expression patterns across 29 cancer types.

bioRxiv · Mar 22, 2024Cancer Biology

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