Author

Thomas Dougherty

California Institute of TechnologyORCID 0009-0006-9981-2018Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 1 Approach:deep learning· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Adam Kagel· 1 shared Alex KongiD· 1 shared Brianna J. McIntoshiD· 1 shared Chan Hong· 1 shared Christine Camacho FullawayiD· 1 shared Cole PavelchekiD· 1 shared David Van ValeniD· 1 shared Erick MoeniD· 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

Adam Kagel

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2 papers

Recent·Most cited

BioinformaticsNature BiotechnologyPeer-reviewedNov 18, 2021

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Noah F. Greenwald, Geneva Miller, Erick Moen…·David Van Valen

The paper introduces Mesmer, a deep learning algorithm for cell segmentation in tissue images, achieving human-level performance and enabling automated feature extraction.

954 citationstop 1%

humanimaging

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BioinformaticsbioRxivPreprintJun 18, 2020

Predicting Antibody Developability from Sequence using Machine Learning

Xingyao Chen, Thomas Dougherty, Chan Hong…·Ian Kerman

This paper develops a machine learning pipeline to predict antibody developability from sequence, achieving high sensitivity and specificity on a dataset of 2400 antibodies.

26 citations

protein sequencemachine learning (classical)

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