Dataset Information

Application of the back-error propagation artificial neural network (BPANN) on genetic variants in the PPAR-? and RXR-? 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-? and RXR-? 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-? and RXR-? 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), serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 (rs4240711, rs4842194, rs2920502 and rs3856806) models were the best models (cross-validation consistency 10/10, P = 0.0107) with the GMDR method. In conclusion, the interaction of the PPAR-? and RXR-? gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.

SUBMITTER: Zhao X

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

REPOSITORIES: biostudies-literature

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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.

Zhao Xu X Xu Kang K Shi Hui H Cheng Jinluo J Ma Jianhua J Gao Yanqin Y Li Qian Q Ye Xinhua X Lu Ying Y Yu Xiaofang X Du Juan J Du Wencong W Ye Qing Q Zhou Ling L

Journal of biomedical research 20130320 2

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 ...[more]

PMID: 24683409

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Project description:AimsOur 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 methodsWe 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.ResultsThe results from BPANN were compared with multiple logistic regression analysis, and the generalized multifactor dimensionality reduction (GMDR) method was used to calculate the joint effects of ADIPOQ, PPAR-γ, and RXR-α genes. By BPANN analysis, the sequence according to the importance of the T2DM risk factors was in the order of serum adiponectin level, rs3856806, rs7649121, hypertension, rs3821799, rs17827276, rs12495941, rs4240711, age, rs16861194, waist circumference, rs2241767, rs2920502, rs1063539, alcohol drinking, smoking, hyperlipoproteinemia, gender, rs3132291, T2DM family history, rs4842194, rs822394, rs1801282, rs1045570, rs16861205, rs6537944, body mass index, rs266729, and rs1801282. However, compared with multiple logistic regression analysis, only 11 factors were statistically significant. After overweight and obesity were taken as environment adjustment factors into the analysis, model A2 B4 C5 C6 C8 (rs3856806, rs4240711, rs7649121, rs3821799, rs12495941) was the best model (coefficient of variation consistency=10/10, P=0.0107) in the GMDR method.ConclusionsThese results suggested the interactions of ADIPOQ, PPAR-γ, and RXR-α genes might play a role in susceptibility to T2DM. BPANN could be used to analyze the risk factors of diseases and provide more complicated relationships between inputs and outputs.

Project description:Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.

Dataset Information

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

Publications

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.

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