Author

Tate Tunstall

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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Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 1 Assay:electronic health records· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Akash KumariD· 1 shared Kate Im· 1 shared Lindsay Meyers· 1 shared Lu Yang· 1 shared Maria-Renee Coldagelli· 1 shared Matthew RabinowitziD· 1 shared Mayowa A. OsundijiiD· 1 shared Naveen Pereira· 1 shared

Activity by Year

2026-2026 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 36 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

Akash KumarORCID

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1 paper

Recent·Most cited

Early identification of rare disease using deep phenotyping of electronic health records

Tate Tunstall, Pauline C. Ng, Lindsay Meyers, Maria-Renee Coldagelli, Lu Yang, Naveen Pereira, Mayowa A. Osundiji, Kate Im, Akash Kumar, Matthew Rabinowitz

This study uses machine learning on electronic health records to identify rare genetic disorders early, achieving up to 89% early detection with over a year lead time for some conditions.

medRxiv · Sep 16, 2026 · PreprintGenetic & Genomic Medicine