Author

Thomas Colthurst

Google (United States)ORCID 0000-0003-3310-5493Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Cory Y. McLeaniD· 2 shared Alexander Ku· 1 shared Andrew W. CarrolliD· 1 shared Babak AlipanahiiD· 1 shared Dan Newburger· 1 shared David H. Alexander· 1 shared Farhad HormozdiariiD· 1 shared Jojo Dijamco· 1 shared

Activity by Year

2018-2021 · peak 1/yr

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10 top collaborators · 27 mutual co-author links

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2 shared papersGenetics

Cory Y. McLeanORCID

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2 papers

Recent·Most cited

GeneticsbioRxivPreprintMay 26, 2021

DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power

Zachary Ryan McCaw, Thomas Colthurst, Taedong Yun…·Farhad Hormozdiari

DOI 10.1101/2021.05.26.445783

humanGWAS summary statistics

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GenomicsNature BiotechnologyPeer-reviewedSep 24, 2018

Creating a universal SNP and small indel variant caller with deep neural networks

Ryan E. Poplin, Pi-Chuan Chang, David H. Alexander…·Mark A. DePristo

A convolutional network on read-pileup images outperforms hand-built statistical variant callers, won an FDA challenge award for SNPs, learns new sequencing technologies and generalizes to other mammals.

1,831 citationstop 1%

whole-genome sequencingdeep learning

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