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The arrays of the decision matrix were normalized as revealed in Table 9.

In view of the small fold changes expected from complex tissues such as brain, an extensive biomathematical workup including RMA-based normalization, fitting with a linear model, statistical ANOVA-based evaluation, stringent filtering, and representation of individual transcript changes per brain region in a decision matrix were applied to suppress background and to reveal the consistent effects.

The normalized decision matrix is shown in Table 5.

The decision matrix is used to describe a multi-criteria decision analysis (MCDA) problem.

In the next stage, the fuzzy decision matrix was calculated using Eqs.

Here normalized fuzzy decision matrix is multiplied with weights of the evaluation attributes.

To make the performance indices dimensionless and comparable, the decision matrix is normalized.

The fuzzy decision matrix was normalized using Eqs. 12 and 13.

Finally, the resulting scores (decision matrix) are used to rank the routes using TOPSIS methodology.

The normalization process of the decision matrix is performed according to the linear normalization algorithm.

Hence, in this step the aggregation of the normalized decision matrix is employed.