Graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns

Journal of Machine Learning Research

Volumn 9, Issue , 2008, Pages 651-682

  Chen, Shann Ching ^a   Gordon, Geoffrey J ^b   Murphy, Robert F ^c  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b Carnegie Mellon University   (United States)

^c CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Approximate inference algorithms; Factor graphs; Location proteomics; Protein subcellular location patterns; Structured classification

Indexed keywords

ALGORITHMS; BIOINFORMATICS; BIOMOLECULES; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL GEOMETRY; COMPUTATIONAL METHODS; COMPUTER NETWORKS; CRACK PROPAGATION; EVOLUTIONARY ALGORITHMS; FUNCTION EVALUATION; GRAPH THEORY; GRAPHIC METHODS; HIDDEN MARKOV MODELS; IMAGE ENHANCEMENT; INFERENCE ENGINES; LEAD; MARKOV PROCESSES; METROPOLITAN AREA NETWORKS; NETWORK PROTOCOLS; PIGMENTS; PROTEINS;

(+ MOD 2N) OPERATION; (ALGORITHMIC) COMPLEXITY; BALANCE (WEIGHTING); BELIEF PROPAGATION (BP); CLASSIFICATIO N PROBLEMS; CLASSIFICATION ACCURACIES; COMPUTATIONAL BIOLOGY; EFFICIENT ALGORITHMS; EXPRESSIVE POWER; FACTOR GRAPHS; GRAPH STRUCTURES; GRAPHICAL MODELS (GM); MARKOV NETWORKS; MARKOV RANDOM FIELD (MRF); NEW CLASS; POTENTIAL FUNCTIONS; PROTEIN SUBCELLULAR LOCATION; SUBCELLULAR LOCATIONS; SYNTHESIS (OF CHIRAL IONIC LIQUIDS);

MATHEMATICAL MODELS;

EID: 44649153825     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

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