Robust gene expression-based classification of cancers without normalization
Keyword(s):
AbstractBinary classification using gene expression data is commonly used to stratify cancers into molecular subgroups that may have distinct prognoses and therapeutic options. A limitation of many such methods is the requirement for comparable training and testing data sets. Here, we describe and demonstrate a self-training implementation of probability ratio-based classification prediction score (PRPS-ST) that facilitates the porting of existing classification models to other gene expression data sets. We demonstrate its robustness through application to two binary classification problems in diffuse large B-cell lymphoma using a diverse variety of gene expression data types and normalization methods.
2019 ◽
Vol 80
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pp. 121-127
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Keyword(s):
2006 ◽
Vol 13
(3)
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pp. 567-576
2008 ◽
Vol 22
(08)
◽
pp. 1587-1598
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