A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

Volumn , Issue , 2014, Pages 804-807

  Akutekwe, Arinze ^a   Seker, Huseyin ^a  

^a DE MONTFORT UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; BIOMARKERS; COMPUTATIONAL EFFICIENCY; DECISION TREES; DIAGNOSIS; EXPERT SYSTEMS; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; LEARNING ALGORITHMS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

CLASSIFICATION METHODS; DYNAMIC BAYESIAN NETWORKS; HYPERTENSION DISEASE; MACHINE LEARNING TECHNIQUES; PREDICTIVE ABILITIES; SUPPORT VECTOR MACHINE RECURSIVE FEATURE ELIMINATIONS; TEMPORAL ASSOCIATION; TEMPORAL RELATIONSHIPS;

GENE EXPRESSION;

ABC TRANSPORTER; ABCG1 PROTEIN, HUMAN; FORKHEAD TRANSCRIPTION FACTOR; FOXQ1 PROTEIN, HUMAN; FUCOSYLTRANSFERASE; FUT6 PROTEIN, HUMAN; GENETIC MARKER; NR2E3 PROTEIN, HUMAN; ORPHAN NUCLEAR RECEPTOR; TRANSCRIPTOME;

ADULT; BAYES THEOREM; CLASSIFICATION; GENE LOCUS; GENETIC MARKER; GENETICS; HUMAN; HUMAN GENOME; HYPERTENSION; MALE; METABOLISM; MICROARRAY ANALYSIS; MIDDLE AGED; SUPPORT VECTOR MACHINE;

ADULT; ATP-BINDING CASSETTE TRANSPORTERS; BAYES THEOREM; FORKHEAD TRANSCRIPTION FACTORS; FUCOSYLTRANSFERASES; GENETIC LOCI; GENETIC MARKERS; GENOME, HUMAN; HUMANS; HYPERTENSION; MALE; MICROARRAY ANALYSIS; MIDDLE AGED; ORPHAN NUCLEAR RECEPTORS; SUPPORT VECTOR MACHINE; TRANSCRIPTOME;

EID: 84929484991     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EMBC.2014.6943713     Document Type: Conference Paper

References (16)

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