A critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancer.

PloS One
Christine StaigerLodewyk F A Wessels

Abstract

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data se...Continue Reading

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Methods Mentioned

BETA
feature extraction
chip
two hybrid
dissection
features extraction

Software Mentioned

HPRD
Affymetrix
NetC
MINT
R NNET SciKits
PinnacleZ

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