Author

Kaipo Tamura

University of Washington

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 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:proteomics· 2 Research Focus:variant calling· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1

Frequent Co-Authors

William Stafford NobleiD· 2 shared Brendan MacLeaniD· 1 shared Brian S. PrattiD· 1 shared Damon MayiD· 1 shared Erin Shammel BakeriD· 1 shared Jefferey L. BurgessiD· 1 shared Karen Richey· 1 shared Kaylie I. Kirkwood‐DonelsoniD· 1 shared

Activity by Year

2018-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

William Stafford NobleORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Development and Application of Multidimensional Lipid Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity

Kaylie I. Kirkwood‐Donelson, Michael W. Christopher, Jefferey L. Burgess, Sally R. Littau, Kevin N Foster, Karen Richey, Brian S. Pratt, Nicholas Shulman, Kaipo Tamura, Michael J. MacCoss, Brendan MacLean, Erin Shammel Baker

The paper develops a multidimensional lipid library using Skyline to annotate lipidomic data and applies it to identify lipid markers for smoke inhalation injury severity.

Journal of Proteome Research · Dec 7, 2021Peer-reviewedMolecular Biology

Detecting modifications in proteomics experiments with Param-Medic

Damon May, Kaipo Tamura, William Stafford Noble

The paper introduces a method to automatically detect modifications in proteomics experiments from mass spectra and provides open-source implementations.

Journal of Proteome Research · Feb 4, 2019Peer-reviewedBioinformatics

An averaging strategy to reduce variability in target-decoy estimates of false discovery rate

Uri Keich, Kaipo Tamura, William Stafford Noble

The paper introduces an averaging strategy to reduce variability in FDR estimates from target-decoy competition in shotgun proteomics.

Journal of Proteome Research · Dec 18, 2018Peer-reviewedBioinformatics