Author

Ning Cao

University of WashingtonORCID 0000-0002-3929-8604Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Ning Cao1 paper · University of California, Los Angeles

Biology Disciplines

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Biochemistry· 1 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:imputation· 1 Organism:mouse· 1

Frequent Co-Authors

Alec Morimoto· 2 shared Benjamin A. ShaveriD· 2 shared Christine C. WuiD· 2 shared Danielle P. Johnson EricksoniD· 2 shared Michael E. RiffleiD· 2 shared Michael J. MacCossiD· 2 shared Nicholas ShulmaniD· 2 shared William Stafford NobleiD· 2 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBiochemistry

Alec Morimoto

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2 papers

Recent·Most cited

A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.

Alex Zelter, Michael E. Riffle, Gennifer E. Merrihew, Batool Mutawe, Nicholas Shulman, Justin A. Sanders, William Stafford Noble, Danielle P. Johnson Erickson, Alec Morimoto, Benjamin A. Shaver, Taylor N Steins, Ning Cao, Eric C Ford, Paul A. Rudnick, Daniel Chelsky, , Michael J. MacCoss

The paper presents two large-scale proteomics datasets from mice exposed to varying X-ray doses and time points, enabling biomarker discovery and machine learning applications for radiation exposure assessment.

bioRxiv · May 22, 2026 · PreprintBiochemistry

Improved quantitation in data-independent acquisition proteomics via retention time boundary imputation

Lincoln Harris, Michael E. Riffle, Nicholas Shulman, William E. Fondrie, Christine C. Wu, Danielle P. Johnson Erickson, Alec Morimoto, Benjamin A. Shaver, Taylor N. Stein, Ning Cao, Eric C. Ford, William Stafford Noble, Michael J. MacCoss

This paper introduces a method to improve quantitation in DIA proteomics by imputing peptide retention time boundaries, leading to more accurate quantifications and better estimation of radiation exposure.

bioRxiv · May 27, 2025 · PreprintBioinformatics