Faster mass spectrometry-based protein inference: Junction trees are more efficient than sampling and marginalization by enumeration

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 9, Issue 3, 2012, Pages 809-817

  Serang, Oliver ^a   Noble, William Stratford ^b  

^a BOSTON CHILDREN S HOSPITAL   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Bayesian inference; Graphical models; Mass spectrometry; Protein identification

Indexed keywords

BAYESIAN INFERENCE; COMPLEX MIXTURE; CONNECTED SUBGRAPHS; EXPECTATION MAXIMIZATION; GRAPHICAL MODEL; INFERENCE PROBLEM; JUNCTION TREES; MARGINALIZATION; MARKOV CHAIN MONTE CARLO; PROTEIN IDENTIFICATION; PYTHON CODE; RATES OF CONVERGENCE; STATISTICAL INFERENCE; TANDEM MASS SPECTROMETRY;

BAYESIAN NETWORKS; FORESTRY; GRAPHIC METHODS; INFERENCE ENGINES; MASS SPECTROMETRY; SPEECH RECOGNITION; STATISTICAL METHODS; TREES (MATHEMATICS);

PROTEINS;

ACOUSTICS; GRAPHIC METHODS; IDENTIFICATION; MASS SPECTROMETERS; MATHEMATICAL MODELS; PROTEINS; STATISTICAL METHODS;

PROTEIN;

ALGORITHM; ARTICLE; CHEMISTRY; MASS SPECTROMETRY; METHODOLOGY; MONTE CARLO METHOD; PROBABILITY;

ALGORITHMS; MARKOV CHAINS; MASS SPECTROMETRY; MONTE CARLO METHOD; PROTEINS;

EID: 84859198472     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2012.26     Document Type: Article

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