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Patients and normals are often categorized in groups according to their SNP genotypes (categorical values).
Hence, the numerical values can be converted to categorical values.
For all categorical variables, binary dummy variables were introduced to represent the categorical values.
Chi-square and Fisher's exact tests were used to analyze categorical values where appropriate.
Categorical values were expressed as proportions and compared using Chi-squared tests.
The present work proposes a SOM architecture that directly processes the categorical values, without the need of any previous transformation.
We used Chi-square test for categorical values and Mann–Whitney U test for continuous data in comparisons.
The more the number of differences in categorical values of X and Y, more the different two objects are.
Continuous variables are reported as mean ± standard deviation, rates and categorical values are reported as subjects-counts and percentage.
The big number of (categorical) values for many of the prediction variables makes visualizing the generated models clearly difficult.
It is for this reason that the categorical values are commonly converted into a binary code, a solution that unfortunately distorts the network training and the posterior analysis.
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