Author

Abhik Lahiri

PathAI (United States)

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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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

Aditya Khosla· 1 shared Amaro N Taylor-WeineriD· 1 shared Andrew H. BeckiD· 1 shared Benjamin GlassiD· 1 shared Chirag Maheshwari· 1 shared Hunter L. Elliott· 1 shared Ilan N. Wapinski· 1 shared James A. DiaoiD· 1 shared

Activity by Year

2020-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Aditya Khosla

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Author profile →

1 paper

Recent·Most cited

Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer

James A. Diao, Wan Fung Chui, Jason K. Wang, Richard N. Mitchell, Sudha K. Rao, Murray B. Resnick, Abhik Lahiri, Chirag Maheshwari, Benjamin Glass, Victoria Mountain, Jennifer K. Kerner, Michael Montalto, Aditya Khosla, Ilan N. Wapinski, Andrew H. Beck, , Hunter L. Elliott

This paper develops a method to predict molecular phenotypes from cancer pathology images using interpretable features, showing correlations with tumor microenvironment markers and predictive power for molecular signatures.

bioRxiv · Aug 2, 2020 · PreprintBioinformatics