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Table 2 Overview of Evaluated Methods

From: Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework

Guo et al. [28] Abbreviation: Mean
Computational basis: Pathway activity
Description: Create pathway-specific gene expression profiles; for each pathway, pathway activity for sample is its mean expression value among all member genes; a classifier is trained on pathway activity profile.
Guo et al. [28] Abbreviation: Median
Computational basis: Pathway activity
Description: Create pathway-specific gene expression profiles; for each pathway, pathway activity for sample is its median expression value among all member genes; a classifier is trained on pathway activity profile.
Bild et al. [45] Abbreviation: PCA
Computational basis: Pathway activity
Description: Create pathway-specific gene expression profiles; for each pathway, top principal component is calculated as the pathway activity; a classifier is trained on pathway activity profile.
Lee et al. [19] Abbreviation: CORGs
Computational basis: Pathway activity
Description: Create pathway-specific gene expression profiles; for each pathway, apply t-test to rank genes and perform a greedy search to find a subset of genes whose averaged expression values is locally maximal in t-test value; a classifier is trained on pathway activity profile; only applicable for two-class problems.
Ainali et al. [72] Abbreviation: Per_pathway
Computational basis: Single genes
Description: Create pathway-specific gene expression profiles; a classifier is trained on each pathway-specific gene expression profile separately, and prediction rates achieved by all pathway classifiers are averaged as the final prediction rate.
Single Genes Abbreviation: SG
Computational basis: Single genes
Description: Apply [71] to select a subset of top genes; a classifier is trained on reduced gene expression profile
Proposed in this work Abbreviation: DIGS
Computational basis: Pathway activity
Description: Create pathway-specific gene expression profiles; Apply the proposed DIGS model to construct pathway activity as weighted linear summation of gene expressions; a classifier is trained on pathway activity profile.
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