Author

David H. Alexander

Google (United States)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Genomics· 2

Signature Assays, Organisms & Methods

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Research Focus:variant calling· 4 Assay:whole-genome sequencing· 4 Research Focus:benchmarking· 2 Approach:deep learning· 2 Approach:statistical model· 2 Organism:bacteria (other)· 1 Research Focus:batch correction / integration· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Cory Y. McLeaniD· 2 shared Kenneth L. LangeiD· 2 shared Mark A. DePristoiD· 2 shared Pi-Chuan ChangiD· 2 shared Ryan E. Poplin· 2 shared Akosua BusiaiD· 1 shared Alexander Ku· 1 shared Benjamin B. ChuiD· 1 shared

Activity by Year

2016-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersBioinformatics

Cory Y. McLeanORCID

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Author profile →

4 papers

Recent·Most cited

Unsupervised Discovery of Ancestry Informative Markers and Genetic Admixture Proportions in Biobank-Scale Data Sets

Seyoon Ko, Benjamin B. Chu, Daniel Peterson, Chidera Okenwa, Jeanette C. Papp, David H. Alexander, Eric M. Sobel, Hua Zhou, Kenneth L. Lange

This paper introduces an unsupervised framework for selecting ancestry informative markers and estimating admixture proportions in large biobank datasets, demonstrating scalability and effectiveness.

The American Journal of Human Genetics · Jan 6, 2023Peer-reviewedBioinformatics

A deep learning approach to pattern recognition for short DNA sequences

Akosua Busia, George E. Dahl, Clara Fannjiang, David H. Alexander, Elizabeth H. Dorfman, Ryan E. Poplin, Cory Y. McLean, Pi-Chuan Chang, Mark A. DePristo

A convolutional network trained on 16S reads from 13,838 species reaches within 2.0% of training-set Bayes-optimal species accuracy and usually beats k-mer, alignment and Kraken2 on held-out-species genera.

bioRxiv · Aug 10, 2019 · PreprintBioinformatics✦ In-depth digest

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

Ryan E. Poplin, Pi-Chuan Chang, David H. Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah Tootoonchi Afshar, Sam S Gross, Lizzie Dorfman, Cory Y. McLean, 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.

Nature Biotechnology · Sep 24, 2018Peer-reviewedGenomics

Efficient analysis of large datasets and sex bias with ADMIXTURE

Suyash S. Shringarpure, Carlos D. Bustamante, Kenneth L. Lange, David H. Alexander

The paper improves ADMIXTURE software for faster ancestry inference and better handling of sex-biased admixture in large genomic datasets.

bioRxiv · Feb 10, 2016Genomics