Empirical bayesian random censoring threshold model improves detection of differentially abundant proteins

Journal of Proteome Research

Volumn 13, Issue 9, 2014, Pages 3871-3880

  Koopmans, Frank ^a,b   Cornelisse, L Niels ^a   Heskes, Tom ^b   Dijkstra, Tjeerd M H ^b  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

^b RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

label free quantification; mass spectrometry; missing data; proteomics

Indexed keywords

PEPTIDE; PROTEIN;

ARTICLE; BAYESIAN LEARNING; EMPIRICAL BAYESIAN RANDOM CENSORING THRESHOLD MODEL; K NEAREST NEIGHBOR; MACHINE LEARNING; PROTEIN ANALYSIS; SINGULAR VALUE THRESHOLDING; BAYES THEOREM; CHEMISTRY; MASS SPECTROMETRY; PROCEDURES; PROTEOMICS; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL MODEL;

BAYES THEOREM; MASS SPECTROMETRY; MODELS, STATISTICAL; PROTEINS; PROTEOMICS; ROC CURVE;

EID: 84914145820     PISSN: 15353893     EISSN: 15353907     Source Type: Journal    
DOI: 10.1021/pr500171u     Document Type: Article

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