The region-based association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease. Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls. To tackle the problem of the sparse distribution, a two-stage approach was proposed in literature: In the first stage, haplotypes are computationally inferred from genotypes, followed by a haplotype coclassification. In the second stage, the association analysis is performed on the inferred haplotype groups. If a haplotype is unevenly distributed between the case and control samples, this haplotype is labeled as a risk haplotype. Unfortunately, the in-silico reconstruction of haplotypes might produce a proportion of false haplotypes which hamper the detection of rare but true haplotypes. Here, to address the issue, we propose an alternative approach: In Stage 1, we cluster genotypes instead of inferred haplotypes and estimate the risk genotypes based on a finite mixture model. In Stage 2, we infer risk haplotypes from risk genotypes inferred from the previous stage. To estimate the finite mixture model, we propose an EM algorithm with a novel data partition-based initialization. The performance of the proposed procedure is assessed by simulation studies and a real data analysis. Compared to the existing multiple Z-test procedure, we find that the power of genome-wide association studies can be increased by using the proposed procedure.
Today, the success or failure of organizations depends to possess the wisdom of their managers promised that the key to organizational success of the business environment, making the right decisions, and create the ability to work and think towards discrimination of products and services the organization . Seek this research to investigation the relationship between the wisdom management and differentiation strategy for service operations . It was a test of that relationship in light of the results of the analysis of the data collected through the questionnaire distributed on a sample from (98) Director Mangers, head of department and head of division in the General Establishment of Civil Aviation . The research used descriptive st
... Show MoreIn this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and
... Show MoreTransparent nano- coating was prepared by Sol-Gel method from titanium dioxide TiO2 which has the ability to self-cleaning coating used for hospitals, laboratories, and places requiring permanent sterilization. Three primary colors are selected (red, blue, and yellow) as preliminary study to the effect of these colors on the nano-coating. Three traditional oil paints color were used as base, then coated by a layer of TiO2-Sol and deposited on the paints. The optical properties of TiO2-Sol were measured; the maximum absorption wavelength at (λmax=387 nm), the refractive index (n=1.4423) and the energy band gap (Eg=3.2 eV). The structure properties found by X-ray diffraction of TiO
In this article we study a single stochastic process model for the evaluate the assets pricing and stock.,On of the models le'vy . depending on the so –called Brownian subordinate as it has been depending on the so-called Normal Inverse Gaussian (NIG). this article aims as the estimate that the parameters of his model using my way (MME,MLE) and then employ those estimate of the parameters is the study of stock returns and evaluate asset pricing for both the united Bank and Bank of North which their data were taken from the Iraq stock Exchange.
which showed the results to a preference MLE on MME based on the standard of comparison the average square e
... Show MoreThyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise
... Show MoreIn this research, Artificial Neural Networks (ANNs) technique was applied in an attempt to predict the water levels and some of the water quality parameters at Tigris River in Wasit Government for five different sites. These predictions are useful in the planning, management, evaluation of the water resources in the area. Spatial data along a river system or area at different locations in a catchment area usually have missing measurements, hence an accurate prediction. model to fill these missing values is essential.
The selected sites for water quality data prediction were Sewera, Numania , Kut u/s, Kut d/s, Garaf observation sites. In these five sites models were built for prediction of the water level and water quality parameters.
The experimental proton resonance data for the reaction P+48Ti have been used to calculate and evaluate the level density by employed the Gaussian Orthogonal Ensemble, GOE version of RMT, Constant Temperature, CT and Back Shifted Fermi Gas, BSFG models at certain spin-parity and at different proton energies. The results of GOE model are found in agreement with other, while the level density calculated using the BSFG Model showed less values with spin dependence more than parity, due the limitation in the parameters (level density parameter, a, Energy shift parameter, E1and spin cut off parameter, σc). Also, in the CT Model the level density results depend mainly on two parameters (T and ground state back shift energy, E0), which are app
... Show More