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A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

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dc.contributor.author Valavanis, IK en
dc.contributor.author Mougiakakou, SG en
dc.contributor.author Grimaldi, KA en
dc.contributor.author Nikita, KS en
dc.date.accessioned 2014-03-01T01:32:28Z
dc.date.available 2014-03-01T01:32:28Z
dc.date.issued 2010 en
dc.identifier.issn 1471-2105 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/20147
dc.subject Artificial Neural Network en
dc.subject Back Propagation en
dc.subject Body Mass Index Bmi en
dc.subject Cardiovascular Disease en
dc.subject Classification Accuracy en
dc.subject Complex Traits en
dc.subject Cross Validation en
dc.subject Genetic Algorithm en
dc.subject Genetic Variation en
dc.subject Genetics en
dc.subject Hybrid Method en
dc.subject Prediction Model en
dc.subject Receiver Operating Characteristic Curve en
dc.subject Training Algorithm en
dc.subject Feed Forward en
dc.subject Neural Network en
dc.subject Risk Factors en
dc.subject.classification Biochemical Research Methods en
dc.subject.classification Biotechnology & Applied Microbiology en
dc.subject.classification Mathematical & Computational Biology en
dc.subject.other GENE-GENE INTERACTIONS en
dc.subject.other BODY-MASS INDEX en
dc.subject.other SINGLE NUCLEOTIDE POLYMORPHISMS en
dc.subject.other ENVIRONMENT INTERACTIONS en
dc.subject.other CARDIOVASCULAR-DISEASE en
dc.subject.other ARCHITECTURE en
dc.subject.other PREDICTION en
dc.subject.other MORTALITY en
dc.subject.other MODEL en
dc.subject.other SNPS en
dc.title A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context en
heal.type journalArticle en
heal.identifier.primary 10.1186/1471-2105-11-453 en
heal.identifier.secondary http://dx.doi.org/10.1186/1471-2105-11-453 en
heal.identifier.secondary 453 en
heal.language English en
heal.publicationDate 2010 en
heal.abstract Background: Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.Results: PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets.Conclusions: The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics. © 2010 Valavanis et al; licensee BioMed Central Ltd. en
heal.publisher BIOMED CENTRAL LTD en
heal.journalName BMC Bioinformatics en
dc.identifier.doi 10.1186/1471-2105-11-453 en
dc.identifier.isi ISI:000282655900002 en
dc.identifier.volume 11 en


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