Gene-nutrition interactions in the onset of obesity as cardiovascular disease risk factor based on a computational intelligence method

Valavanis, IK; Mougiakakou, SG; Marinos, S; Karkalis, G; Grimaldi, KA; Gill, R; Nikita, KS

dc.contributor.author	Valavanis, IK	en
dc.contributor.author	Mougiakakou, SG	en
dc.contributor.author	Marinos, S	en
dc.contributor.author	Karkalis, G	en
dc.contributor.author	Grimaldi, KA	en
dc.contributor.author	Gill, R	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:45:17Z
dc.date.available	2014-03-01T02:45:17Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32259
dc.subject	Artificial Neural Network	en
dc.subject	Body Mass Index Bmi	en
dc.subject	Cardiovascular Disease	en
dc.subject	Cardiovascular Disease Risk Factor	en
dc.subject	Complex Traits	en
dc.subject	Computational Intelligence	en
dc.subject	Cross Validation	en
dc.subject	gene-environment interaction	en
dc.subject	Genetic Variation	en
dc.subject	Genetics	en
dc.subject	System Evaluation	en
dc.subject	Risk Factors	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Body mass index	en
dc.subject.other	Cardio-vascular disease risk factors	en
dc.subject.other	Cardiovascular disease	en
dc.subject.other	Complex traits	en
dc.subject.other	Computational intelligence	en
dc.subject.other	Computational intelligence methods	en
dc.subject.other	Cross validation	en
dc.subject.other	Gene-environment interaction	en
dc.subject.other	Genetic information	en
dc.subject.other	Genetic variation	en
dc.subject.other	Human obesity	en
dc.subject.other	Input variables	en
dc.subject.other	Output variables	en
dc.subject.other	Resampling technique	en
dc.subject.other	Risk factors	en
dc.subject.other	System use	en
dc.subject.other	Backpropagation	en
dc.subject.other	Bioinformatics	en
dc.subject.other	Chemical vapor deposition	en
dc.subject.other	Neural networks	en
dc.subject.other	Nutrition	en
dc.title	Gene-nutrition interactions in the onset of obesity as cardiovascular disease risk factor based on a computational intelligence method	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/BIBE.2008.4696678	en
heal.identifier.secondary	4696678	en
heal.identifier.secondary	http://dx.doi.org/10.1109/BIBE.2008.4696678	en
heal.publicationDate	2008	en
heal.abstract	Identification of gene-gene and gene-environment interactions that contribute in the onset of a multi-factorial disease supports the prevention of diseases like the Cardiovascular Disease (CVD). Body Mass Index (BMI), a measure of human obesity, is an independent risk factor of CVD. Furthermore, it is known that a subject's BMI is affected both by his/her lifestyle, e.g. nutrition, and genetic profile. Aim of the paper is to predict a subject's onset of obesity using lifestyle and genetic information. The prediction is performed by a computational intelligence based system using a Parameter Decreasing Method (PDM) combined with an Artificial Neural Network (ANN). The system uses an initial set of 63 input variables corresponding to sex, average nutrition intake measurements, and genetic variations to identify the 32 most important ones that affect BMI. The selected variables are the ones to interact with each other towards the complex trait of BMI, which is used as a 2-class output variable (BMI ≤ 25 vs. BMI>25) in the ANN. The system achieved a mean accuracy of the system evaluated by a 3-cross validation resampling technique equal to 77.89%.	en
heal.journalName	8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008	en
dc.identifier.doi	10.1109/BIBE.2008.4696678	en