Application of back propagation artificial neural network on genetic variants in adiponectin ADIPOQ, peroxisome proliferator-activated receptor-γ, and retinoid X receptor-α genes and type 2 diabetes risk in a Chinese Han population.

ABSTRACT:

Aims

Our study was designed to explore the applied characteristics of the back propagation artificial neural network (BPANN) on studying the genetic variants in adipnectin ADIPOQ, peroxisome proliferator-activated receptor (PPAR)-γ, and retinoid X receptor-α (RXR-α) genes and type 2 diabetes mellitus (T2DM) risks in a Chinese Han population.

Subjects and methods

We used BPANN as the fitting model based on data gathered from T2DM patients (n=913) and normal controls (n=1,001). The mean impact value (MIV) for each input variables were calculated, and the sequence of the factors according to their absolute MIVs was sorted.

Results

The results from BPANN were compared with multiple logistic regression analysis, and the generalized multifactor dimensionality reduction (GMD

SUBMITTER: Shi H 

PROVIDER: S-EPMC3284696 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature