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Table 5 Protein complex detection algorithm.

From: Protein complex detection in PPI networks based on data integration and supervised learning method

Input : an unweighted network, a weighted network built via GO annotation and a training set
Complex detection process:
Step 1: construct the feature vector space for the complexes in the training set from the unweighted and weighted PIN networks and train the Regression model
Step 2: find maximal cliques in the PIN by the Cliques algorithm
   -rank the clique set C={C1, C2, ..., Cn} in descending order of the scores given by the Regression model
   -for each clique Ci, check all the cliques (denoted as Cj) with lower scores, if Ci∩Cj > threshold, then remove Cj.
   -output: the updated clique set
Step 3: grow the cliques
   -for each clique Ci, the set of its neighbors is denoted as N(Ci), do update operation as
follows:
      -check all the nodes in N(Ci)
      -select viN(Ci), which makes viCi achieve higher score given by the Regression model
      -update Ci= viCi, N(Ci) = N(Ci) - vi
         -repeat the update operation until there is no node vj in N(Ci) that leads to score(vjCi) > score(Ci)
      -output: the candidate complex set C = {C1, C2, ..., Cn}
Step 4: filter the candidate complexes
   -rank the candidate complexes in descending order of the score given by the Regression model
   -for each candidate complex Ci, check all the candidates Cj with lower scores
         -if overlap (Ci, Cj) > merg_thred
            if score(CiCj) > score(Ci) do merge operation: update Ci = CiCj
            else do remove operation: remove Cj from the candidate set
output: the predicted complex set