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Table 3 Summary of the proposed SEQSCA-algorithm

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: ss+1
Until: s=S;
Set \( \left [ \boldsymbol {M} \right ]_{i,j} = \left [ \boldsymbol {M}^{(S)} \right ]_{i,j} / n^{(S)}_{i,j} \, \forall i,j \in \mathcal {G}\)