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When applied to the WTCCC type 1 diabetes data, the method identified many previously known T1D associated genes, including PTPN22, CTLA4, MHC, and IL2RA.
By stepwise refinement on multiple data, the method increases the accuracy of 3D models gradually and effectively.
When applied to homogeneous population data, the method gives posterior probabilities for LD-block boundaries, which not only result in accurate block partitions of SNPs, but also provide measures of partition uncertainty.
Having been tested against both simulated and real radiation monitoring data, the method was found to be able to realistically reconstruct the real scene of dispersion, as well as the uncertain parameters.
For well-behaved data sets, i.e. monotonic convergence with the expected observed order of grid convergence and no scatter in the data, the method reduces to the well known Grid Convergence Index.
For the application on LF-NMR relaxation data, the method has two basic requirements in practice: (1) two or more samples must be analyzed simultaneously and (2) all samples must contain the same qualities (i.e., identical sets of distinct T2 values).
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As with individual data, the methods of adjustments are reported (Dec ID and Inc ID).
To check these data, the methods were used on the original data (n = 185).
Despite exponentially increasing data, the methods for studying PSC remain variable.
The description of the data and the method of analysis are presented in "Data" and "Methods" section.
*The count method uses only numerator data; the rate method uses numerator and denominator data.
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