Table 2

Algorithm 2 Random GO walk

1

inputs:

      d, a new document

      G, a GO graph with training cases

      nSample, the sample size

      nMaxSteps, the number of maximum steps

2

initialize:

3

   finalLeaves ← {}

4

   finalLeavesProbs ← {}

5

for n in 1: nSamples

6

   g ← initialize()

7

      //Select initial GO node randomly

   for s in 1: nMaxSteps:

8

      T ← TempFunc(s)

9

      nbrs ← G.neighbors(g)

10

      g* ← q(g*|g, d)

         //Sample from proposal distribution, g*∈{g, nbrs}

11

      u ← uniform [0, 1]

12

      if u <A ←

13

         g ← g*

14

   end

15

   finalLeaves ← union(finalLeaves, curNode)

16

end

17

finaLeavesProbs ← calProbFromSample(finalLeaves)

18

outputs: finalLeaves, finaLeavesProbs

Jin et al. BMC Bioinformatics 2008 9:525   doi:10.1186/1471-2105-9-525