Table 2 Summary of the tissue identification capabilities of most related methods

AGEP

Good classifier. Results available per gene, with a biologically meaningful distance metric.

Computationally intensive. Weight of all genes equal.

93.6% accuracy

96.9% combined accuracy

NN

Relatively robust and easy to setup.

Very sensitive to the selection of parameters and the distance metric chosen. No simple choice for distance metric. No simple way to interpret gene-by-gene contribution to the similairy.

90.2% accuracy

94.4% combined accuracy

SVM

Powerful classifying performance if properly customized for the task

No simple solution for selection of kernel. With complex tasks somewhat subject to overfitting. No gene-by-gene contribution available in biologically interpretable manner.

90.4% accuracy NOTE: due to computational limitations was actually 10-fold cross-validation.

98.0% combined accuracy

DNA barcode (Zilliox et al. 2007)

Good classifier. Simple to understand per gene comparison.

Per gene classification is binary, missing out a lot of the variation.

Not tested

Not tested

Cancer molecular classification (Parmigiani et al. 2002)

Good classifier. Simple to understand per gene comparison.

Per gene classification is ternary, missing out a lot of the variation.

Not tested

Not tested

Probabilistic retrieval and visualization of biologically relevant microarray experiments (Caldas et al. 2009)

Good at finding experiments that repeat biological responses.

Works for gene sets derived from comparative experiments

N/A

N/A