Empirical Bayesian Random Censoring Threshold Model Improves Detection of Differentially Abundant Proteins

F.T.W. Koopmans, L.N. Cornelisse, T. Heskes, T.M.H. Dijkstra

Research output: Contribution to journal › Article › Academic › peer-review

Original language	Undefined/Unknown
Pages (from-to)	3871-3880
Journal	Journal of Proteome Research
Volume	13
Issue number	9
DOIs	https://doi.org/10.1021/pr500171u
Publication status	Published - 2014

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title = "Empirical Bayesian Random Censoring Threshold Model Improves Detection of Differentially Abundant Proteins",

author = "F.T.W. Koopmans and L.N. Cornelisse and T. Heskes and T.M.H. Dijkstra",

year = "2014",

doi = "10.1021/pr500171u",

language = "Undefined/Unknown",

volume = "13",

pages = "3871--3880",

journal = "Journal of Proteome Research",

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publisher = "American Chemical Society",

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T1 - Empirical Bayesian Random Censoring Threshold Model Improves Detection of Differentially Abundant Proteins

AU - Koopmans, F.T.W.

AU - Cornelisse, L.N.

AU - Heskes, T.

AU - Dijkstra, T.M.H.

PY - 2014

Y1 - 2014

U2 - 10.1021/pr500171u

DO - 10.1021/pr500171u

M3 - Article

C2 - 25102230

VL - 13

SP - 3871

EP - 3880

JO - Journal of Proteome Research

JF - Journal of Proteome Research

SN - 1535-3893

IS - 9

ER -