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This identified a one gene classifier, which classifies 99% of samples correctly in leave-one-out-crossvalidation (loocv).

Furthermore, we found WT1, a gene described as favorable for survival in MSKCC gene classifier, down-regulated in our list.

Consistent with previously published results, we found up-regulated some genes associated to poor survival and included in different prognostic classifiers, such as BTG2 (Karmanos gene classifier and MSKCC gene classifier) [13], [17], BIRC5 and KIF4A (Karmanos gene classifier), or SEPT9 (Brigham list) [15].

Previously, we identified a candidate progression-related gene classifier that had strong predictive value in terms of disease outcomes in NMIBC [13].

Here we have assessed the ability of a proposed 44 gene classifier [8] to discriminate between CFS patients and healthy control individuals.

Recently, we reported the identification of a progression-related gene classifier that had strong predictive value in terms of disease outcomes in NMIBC [13].

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Similar(38)

The multivariate Cox regression analysis showed that the associations between the three gene classifiers and survival were much poorer than the 23-gene classifier.

To align with the original reports, we divided patients into three (for 35-gene classifier), four (for 44-gene classifier), and two (for 47-gene classifier) groups accordingly.

Alike the 23-gene classifier, the 6-gene clasignificantlyficantly discriminated patients into two survival groups in all the cohorts assessed, while the 11-gene classifier did not work well in two GBM cohorts (UCLA and CMBC).

Moreover, a 41-gene classifier based on expression signature can identify TAA patients with high accuracy.

A 41-gene classifier was identified based on its consistency in classification accuracy (Figure S1).

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