A statistical framework to discover true associations from multiprotein complex pull-down proteomics data sets

Proteins: Structure, Function and Genetics

Volumn 64, Issue 2, 2006, Pages 436-443

  Shen, Changyu ^a   Li, Lang ^a   Chen, Jake Yue ^b,c,d  

^a INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b INDIANA UNIVERSITY   (United States)

^c IN 46202 USA   (United States)

^d Indiana University   (United States)

Author keywords

Binomial Bernoulli (BB); Complete binomial Bernoulli (CBB); High throughput; Multiprotein complex (MPC); Protein protein interaction; Y2H; Yeast two hybrid

Indexed keywords

FUNGAL PROTEIN; MULTIPROTEIN COMPLEX;

ACCURACY; ARTICLE; BAYES THEOREM; BINOMIAL DISTRIBUTION; BIOINFORMATICS; CALCULATION; DATA ANALYSIS; MASS SPECTROMETRY; MULTIPROTEIN COMPLEX PULL DOWN METHOD; NONHUMAN; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; PROTEIN PURIFICATION; PROTEOMICS; SACCHAROMYCES CEREVISIAE; TWO HYBRID SYSTEM;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; FALSE POSITIVE REACTIONS; FUNGAL PROTEINS; MODELS, STATISTICAL; MULTIPROTEIN COMPLEXES; PROTEIN CONFORMATION; PROTEINS; PROTEOME; PROTEOMICS; REPRODUCIBILITY OF RESULTS; SACCHAROMYCES CEREVISIAE; SENSITIVITY AND SPECIFICITY;

EID: 33745620168     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.20994     Document Type: Article

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