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Finally, DCNNs based classifier and multiclass support vector machines (SVMs) classifier are used for classification of single and complex PQDs.

Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.

The MLPNN based classifier outperformed the LR based counterpart.

Secondly, introducing a new eigen value based classifier.

The LDA based classifier showed poor internal validation (Figure S3).

This variant classifier possesses the advantages of both the traditional sparse representation based classifier and the Nearest Neighbor classifier.

Therefore, weighted sparse representation based classifier is superior to support vector machine classifier.

Overall, the accuracy of LDA based classifier achieved 94.96%.

Overall, the Naïve Bayes and logistic regression classifiers performed better than the decision-tree based classifier (Random Forests) and all three classifiers performed significantly better than ZeroR.

Additionally, the filtered gene set classifiers displayed higher specificities (true negative rates) compared to the full gene set based classifiers.

Template based rotation: a method for functional connectivity analysis with a priori templates.