Εξειδίκευση τύπου :	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Mining nutrigenetics patterns related to obesity: Use of parallel multifactor dimensionality reduction
Δημιουργός/Συγγραφέας:	Karayianni K.N. Grimaldi K.A. Nikita K.S. Valavanis I.K.
Εκδότης:	Inderscience Publishers
Ημερομηνία:	2015
Γλώσσα:	Αγγλικά
ISSN:	1744-5485
DOI:	10.1504/IJBRA.2015.069194
Άλλο:	PubMed ID: 26561019
Περίληψη:	This paper aims to enlighten the complex etiology beneath obesity by analysing data from a large nutrigenetics study, in which nutritional and genetic factors associated with obesity were recorded for around two thousand individuals. In our previous work, these data have been analysed using artificial neural network methods, which identified optimised subsets of factors to predict one's obesity status. These methods did not reveal though how the selected factors interact with each other in the obtained predictive models. For that reason, parallel Multifactor Dimensionality Reduction (pMDR) was used here to further analyse the pre-selected subsets of nutrigenetic factors. Within pMDR, predictive models using up to eight factors were constructed, further reducing the input dimensionality, while rules describing the interactive effects of the selected factors were derived. In this way, it was possible to identify specific genetic variations and their interactive effects with particular nutritional factors, which are now under further study.
Τίτλος πηγής δημοσίευσης:	International Journal of Bioinformatics Research and Applications
Τόμος/Κεφάλαιο:	11
Τεύχος:	3
Σελίδες:	233-246
Θεματική Κατηγορία:	[EL] Βιολογία (Γενικά)[EN] Biology (General)
Λέξεις-Κλειδιά:	Data mining Genetic variations Multifactor dimensionality reduction Nutrigenetics Obesity pMDR Predictive models Single nucleotide polymorphism
Αξιολόγηση από ομότιμους (peer reviewed):	Ναι
Κάτοχος πνευματικών δικαιωμάτων:	© 2015 Inderscience Enterprises Ltd.
Εμφανίζεται στις συλλογές:	Ινστιτούτο Χημικής Βιολογίας - Επιστημονικό έργο