An effective, practical and low computational cost framework for the integration of heterogeneous data to predict functional associations between proteins by means of artificial neural networks

Neurocomputing

Volumn 121, Issue , 2013, Pages 64-78

  Florido, J P ^a,b   Pomares, H ^a   Rojas, I ^a   Guillen A ^a   Ortuno, F M ^a   Urquiza, J M ^c  

^a UNIVERSITY OF GRANADA   (Spain)

^b Medical Genome Project   (Spain)

^c BELLVITGE BIOMEDICAL RESEARCH INSTITUTE IDIBELL   (Spain)

Author keywords

Data distribution; Data integration; Functional linkage network; Multilayer Perceptrons; Systems biology

Indexed keywords

DATA DISTRIBUTION; FUNCTIONAL ASSOCIATIONS; FUNCTIONAL RELATIONSHIP; HETEROGENEOUS DATA SOURCES; LEARNING PROCEDURES; OVER FITTING PROBLEM; PREDICTION ACCURACY; SYSTEMS BIOLOGY;

COSTS; FORECASTING; INTEGRAL EQUATIONS; MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS; OPTIMIZATION; PROTEINS;

DATA INTEGRATION;

FUNGAL PROTEIN; PROTEIN YKR089C; UNCLASSIFIED DRUG;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; GENE FUSION; GENE INTERACTION; GENOMIC NEIGHBORHOOD; GENOMICS; GOLD STANDARD; INFORMATION PROCESSING; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MICROARRAY ANALYSIS; MOLECULAR BIOLOGY; PERCEPTRON; PHYLOGENY; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PROTEIN FUNCTION; PROTEIN PROTEIN INTERACTION; SCORING SYSTEM; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SEQUENCE SIMILARITY; YEAST;

EID: 84884153831     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.11.040     Document Type: Article

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