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

Valavanis, IK; Mougiakakou, SG; Grimaldi, KA; Nikita, KS

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