Application of the back-error propagation artificial neural network (BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population.

ABSTRACT: This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propagation artificial neural network (BPANN). We established the model based on data gathered from metabolic syndrome patients (n = 1012) and normal controls (n = 1069) by BPANN. Mean impact value (MIV) for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction (GMDR) confirmed a joint effect of PPAR-γ and RXR-α based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk factors were in the order of body mass index (BMI), se

SUBMITTER: Zhao X 

PROVIDER: S-EPMC3968282 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature