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Table 1 Factors

From: Comparative evaluation of set-level techniques in predictive classification of gene expression samples

Analyzed factors

Alternatives

#Alts

1. Gene sets (Sec.)

Genuine, Random

2

2. Ranking algo (Sec.)

GSEA, SAM-GS, Global

3

3. Set(s) forming features*

1, 2, ... 10,n - 9, n - 8,...n,1:10, n - 9 : n

22

4. Aggregation (Sec.)

SVD, AVG, SetSig, None

4

Product

 

528

Auxiliary factors

Alternatives

# Alts

5. Learning algo (Sec.)

svm, 1-nn, 3-nn, nb, dt

5

6. Dataset (Sec.)

d1 ... d30

30

7. Testing Fold

f1 ... f10

10

Product

 

1500

  1. Alternatives considered for factors influencing the set-level learning workflow. The number left of each factor refers to the workflow step (Fig. 2) in which it acts.
  2. *Identified by rank, n corresponds to the lowest ranking set, i:j denotes that all of gene sets ranking i to j are used to form features.
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BMC Bioinformatics

ISSN: 1471-2105

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