Author

Samuel S. Minot

Primary ORCID profile (0000-0003-1639-3905 · 8 papers)Unclaimed ORCID profile · follow to get notified of new preprints

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Same name, identified by ORCID

Samuel S. MinotiD8 papers · Fred Hutch Cancer Center

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Approach:statistical model· 2 Research Focus:variant calling· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Assay:DNA methylation· 1

Frequent Co-Authors

Dana R. Kadavy· 1 shared Krista L. TernusiD· 1 shared Nicholas B. Greenfield· 1 shared Niklas Krumm· 1 shared Stephen D. Turner· 1 shared

Activity by Year

2014-2015 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 4 mutual co-author links

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1 shared paperBioinformatics

Dana R. Kadavy

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2 papers

Recent·Most cited

One Codex: A Sensitive and Accurate Data Platform for Genomic Microbial Identification

Samuel S. Minot, Niklas Krumm, Nicholas B. Greenfield

One Codex evaluates its microbial identification platform using a large dataset, showing high sensitivity and specificity compared to other methods.

bioRxiv · Sep 28, 2015 · PreprintBioinformatics

SIANN: Strain Identification by Alignment to Near Neighbors

Samuel S. Minot, Stephen D. Turner, Krista L. Ternus, Dana R. Kadavy

SIANN detects target organisms in mixed samples with high specificity and sensitivity, even at low abundances, using alignment to near neighbor genomes.

bioRxiv · Jan 10, 2014 · PreprintBioinformatics