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Based on the traditional eigenvector method, a weighted method is proposed.

As for the second method, a weighted sum-based scalarization is introduced.

In the proposed method, a weighted function is adopted to design the objective function of PSO, which takes into account the average classification accuracy rates (ACC) of SVM, the number of support vectors (SVs) and the selected features simultaneously.

The center vector weighted method A weighted center vector classification method is proposed by Shankar [32], which firstly defines a method of characteristics to distinguish ability, the ability to distinguish between rights and get a new center vector.

Additionally, the estimator of this method, a weighted sum of pairwise estimates, furnishes a defined variance for the time of the most common ancestor between subtypes.

In our method, a weighted graph is introduced to represent PIN as G = (V, w(E)), where w(E) represents the weighted interaction.

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We also describe three other approaches against which we compare our method: a simple K-medoids method (WKM) a weighted K-medoids method, and a previously described hierarchical clustering algorithm designed for aCGH (van Wieringen and van de Wiel, 2008).

Here we propose two methods, a weighted haplotype-based approach and an imputation-based approach, to test for the effect of rare variants with GWAS data.

After trimming, the TMM method takes a weighted mean of M gk r, with weights as the inverse of the approximate asymptotic variances (calculated using the delta method [ 13]).

For test of the stability we used the test-retest method and a weighted Cohen's kappa analysis for ordinal data [22] together with the percentage agreement method [18], a nonparametric statistical test for ordinal data.

The test-retest method with a weighted Cohen's kappa showed a good stability of the instrument.

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