Author

Thomas J. Fuchs

Icahn School of Medicine at Mount SinaiORCID 0000-0001-7603-8687Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Neuroscience· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:deep learning· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Ahmet ZehiriD· 1 shared Andrew J. SchaumbergiD· 1 shared Bangyan Liu· 1 shared Chad VanderbiltiD· 1 shared Csaba KonràdiD· 1 shared Deborah F. DeLairiD· 1 shared Evan Woo· 1 shared Giovanni ManfrediiD· 1 shared

Activity by Year

2016-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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1 shared paperBioinformatics

Ahmet ZehirORCID

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3 papers

Recent·Most cited

NeurosciencebioRxivPreprintOct 23, 2022

Machine learning approaches based on fibroblast morphometry confidently identify stress but have limited ability to predict ALS

Csaba Konràd, Evan Woo, Kirsten G. Bredvik…·Giovanni Manfredi

The paper uses machine learning on fibroblast morphometry to predict stress but finds limited ability to distinguish ALS from controls.

DOI 10.1101/2022.10.23.513410

𝕏 PostBluesky

BioinformaticsbioRxivPreprintSep 16, 2020

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

John Ziegler, Jaclyn Frances Hechtman, Ryan N. Ptashkin…·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.

9 citations

humanwhole-exome sequencing

𝕏 PostBluesky

PathologybioRxivPreprintJul 21, 2016

H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer

Andrew J. Schaumberg, Mark A. Rubin, Thomas J. Fuchs

This paper develops a deep learning model that predicts SPOP mutation status in prostate cancer from H&E-stained whole slide images, achieving high accuracy across different slide types and demonstrating content-based image retrieval for genetic-histologic relationships.

DOI 10.1101/064279

𝕏 PostBluesky