From: Learning directed acyclic graphs from large-scale genomics data
Initialization: M (0)=0 N×N ; \(\phantom {\dot {i}\!}\boldsymbol {M}_{s=0} = \boldsymbol {0}_{N_{S} \times N_{S}}\); frequency counter \(n^{(0)}_{i,j} = 0\) | |
Repeat: | |
1: Select subset \(\mathcal {G}_{s}\) of size N S from \(\mathcal {G}\); draw each gene from \(\mathcal {G}\) with equal probability without replacement | |
2: Update: \(n^{(s+1)}_{i,j} = n^{(s)}_{i,j} + 1\) for all \(i,j \in \mathcal {G}_{s}\) | |
3: Estimate the DAG topology \(\mathcal {E}_{s}\) of set \(\mathcal {G}_{s}\) using GENIE, GI-GENIE, respectively; ⇒M s | |
4: Update reliability matrix M (s) according to Eq. (16) | |
7: Update iteration number: s←s+1 | |
Until: s=S; | |
Set \( \left [ \boldsymbol {M} \right ]_{i,j} = \left [ \boldsymbol {M}^{(S)} \right ]_{i,j} / n^{(S)}_{i,j} \, \forall i,j \in \mathcal {G}\) |