Author

Nicholas C. Mew

University of California, San FranciscoORCID 0000-0001-9848-0396Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Michael J. KeiseriD· 2 shared Elena L. CáceresiD· 1 shared Elizabeth Keiser· 1 shared Garrett GaskinsiD· 1 shared Mikio TadaiD· 1 shared Sina GhandianiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 11 mutual co-author links

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2 shared papersPathology

Michael J. KeiserORCID

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2 papers

Recent·Most cited

PathologyCell Reports MethodsPeer-reviewedOct 1, 2025

Machine-learning convergent melanocytic morphology despite noisy archival slides

Mikio Tada, Garrett Gaskins, Sina Ghandian…·Elizabeth Keiser

This study develops deep-learning models to detect melanocytic atypia from H&E slides using complementary IHC stains, achieving high AUROC and AUPRC scores despite limited paired tissue data.

1 citation

humanimaging

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BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedNov 27, 2020

Adding stochastic negative examples into machine learning improves molecular bioactivity prediction

Elena L. Cáceres, Nicholas C. Mew, Michael J. Keiser

This paper improves molecular bioactivity prediction by introducing stochastic negative examples during training, increasing drug-screening benchmark performance by 121.7%.

33 citations

machine learning (classical)drug response / drug discovery

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