Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions

PLoS Computational Biology

Volumn 7, Issue 7, 2011, Pages

  Xing, Chuanhua ^a   Dunson, David B ^b  

^a DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Duke University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; DATA INTEGRATION; ERROR CORRECTION; INFERENCE ENGINES; PROTEINS;

BAYESIAN INFERENCE; CELLULAR PROCESS; ENSEMBLE LEARNING; FALSE NEGATIVES; FALSE POSITIVE; GENOMIC DATA; HIGH QUALITY; MISCLASSIFICATION RATES; NON-PARAMETRIC BAYES; PROTEIN-PROTEIN INTERACTIONS;

FORECASTING;

ANALYTICAL ERROR; ARTICLE; ARTIFACT REDUCTION; BAYES ENSEMBLE LEARNING; BAYES THEOREM; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; INTERMETHOD COMPARISON; PREDICTION; PROTEIN ANALYSIS; PROTEIN PROTEIN INTERACTION; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL DISTRIBUTION; VALIDATION PROCESS; ALGORITHM; BIOLOGY; CHEMISTRY; CLASSIFICATION; GENETICS; HUMAN; METABOLISM; METHODOLOGY; PROTEIN DATABASE; REPRODUCIBILITY; STATISTICAL MODEL;

PROTEIN;

ALGORITHMS; BAYES THEOREM; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; HUMANS; LOGISTIC MODELS; PROTEIN INTERACTION MAPPING; PROTEINS; REPRODUCIBILITY OF RESULTS; ROC CURVE;

EID: 79960944407     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002110     Document Type: Article

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