Author

P. Raharja-Liu

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 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

A. Geraldes· 2 shared C. Bass· 2 shared D. Mehrotra· 2 shared J. Hense· 2 shared J. Schmidt· 2 shared P. Pandya· 2 shared S. Arslan· 2 shared S. Singhal· 2 shared

Activity by Year

2022-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersCancer Biology

A. Geraldes

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2 papers

Recent·Most cited

Cancer BiologybioRxivPreprintJan 21, 2022

Deep learning can predict multi-omic biomarkers from routine pathology images: A systematic large-scale study

S. Arslan, D. Mehrotra, J. Schmidt…·P. Raharja-Liu

This study shows deep learning can predict multi-omic biomarkers from H&E-stained cancer slides, with 50% of models achieving AUC > 0.633 across 32 cancer types.

DOI 10.1101/2022.01.21.477189

humanimaging

𝕏 PostBluesky

Cancer BiologybioRxivPreprintJan 4, 2022

Evaluation of a predictive method for the H&E-based molecular profiling of breast cancer with deep learning

S. Arslan, X. Li, J. Schmidt…·P. Raharja-Liu

The paper validates a deep learning method that predicts breast cancer biomarkers from H&E slides with high accuracy in a clinical setting.

DOI 10.1101/2022.01.04.474882

humanimaging

𝕏 PostBluesky