Author

Deborah F. DeLair

New York UniversityORCID 0000-0002-9782-6188Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Assay:imaging· 1 Research Focus:variant calling· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Ahmet ZehiriD· 1 shared Chad VanderbiltiD· 1 shared David FenyöiD· 1 shared Gowtham JayakumaraniD· 1 shared Jacklyn CasanovaiD· 1 shared Jaclyn Frances HechtmaniD· 1 shared Jinru ShiaiD· 1 shared John Ziegler· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperBioinformatics

Ahmet ZehirORCID

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2 papers

Recent·Most cited

Predicting Endometrial Cancer Subtypes and Molecular Features from Histopathology Images Using Multi-resolution Deep Learning Models

Runyu Hong, Wenke Liu, Deborah F. DeLair, Narges Razavian, David Fenyö

This paper introduces a deep learning model that predicts endometrial cancer subtypes and mutations from histopathology images, offering a faster alternative to genomic sequencing.

Cell Reports Medicine · Sep 1, 2021Peer-reviewedBioinformatics

MiMSI - a deep multiple instance learning framework improves microsatellite instability detection from tumor next-generation sequencing

John Ziegler, Jaclyn Frances Hechtman, Ryan N. Ptashkin, Gowtham Jayakumaran, Sumit Middha, Shweta S. Chavan, Chad Vanderbilt, Deborah F. DeLair, Jacklyn Casanova, Jinru Shia, Nicole DeGroat, Ryma Benayed, Marc Ladanyi, Michael F. Berger, Thomas J. Fuchs, Ahmet Zehir

MiMSI improves MSI detection in low tumor purity samples using deep learning and multiple instance learning, achieving higher sensitivity and auROC than existing methods.

bioRxiv · Sep 16, 2020 · PreprintBioinformatics