Identifying significant edges in graphical models of molecular networks

Artificial Intelligence in Medicine

Volumn 57, Issue 3, 2013, Pages 207-217

  Scutari, Marco ^a   Nagarajan, Radhakrishnan ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF KENTUCKY COLLEGE OF PUBLIC HEALTH   (United States)

Author keywords

Bayesian networks; Graphical models; L1 norm; Model averaging; Molecular networks

Indexed keywords

BIOLOGICAL SIGNIFICANCE; CUMULATIVE DISTRIBUTION FUNCTION; GRAPHICAL MODEL; L1 NORM; MODEL AVERAGING; MOLECULAR NETWORKS; PROTEIN EXPRESSION PROFILES; STRUCTURE LEARNING ALGORITHM;

ABSTRACTING; GENE EXPRESSION; GRAPHIC METHODS; LEARNING ALGORITHMS; SAMPLING;

BAYESIAN NETWORKS;

PROTEIN;

ACCURACY; ARTICLE; LEARNING ALGORITHM; MOLECULAR BIOLOGY; PERFORMANCE; PRIORITY JOURNAL; PROTEIN EXPRESSION; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; COMPUTER GRAPHICS; FLOW CYTOMETRY; MODELS, THEORETICAL; PROTEINS; SIGNAL TRANSDUCTION;

EID: 84876705387     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2012.12.006     Document Type: Article

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