Author

David E. Heckerman

Microsoft (United States)ORCID 0000-0002-4274-6084Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:ChIP-seq· 1 Assay:CRISPR screen· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:large language model· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Alex C WellsiD· 1 shared Ali TorkamaniiD· 1 shared Amalio TelentiiD· 1 shared Bing ReniD· 1 shared Carl Myers Kadie· 1 shared Julia di IulioiD· 1 shared Yin LiiD· 1 shared

Activity by Year

2018-2019 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 15 mutual co-author links

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1 shared paperGenomics

Alex C WellsORCID

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2 papers

Recent·Most cited

Ludicrous Speed Linear Mixed Models for Genome-Wide Association Studies

Carl Myers Kadie, David E. Heckerman

The paper introduces a cloud-optimized version of FaST-LMM for GWAS, enabling analysis of one million SNPs across one million individuals in two weeks with 868 CPU days.

bioRxiv · Dec 7, 2019 · PreprintBioinformatics

Identification of essential regulatory elements in the human genome

Alex C Wells, David E. Heckerman, Ali Torkamani, Yin Li, Bing Ren, Amalio Telenti, Julia di Iulio

The paper develops a model to identify essential regulatory elements in the human genome using machine learning and validates it with functional screens.

bioRxiv · Oct 16, 2018 · PreprintGenomics