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Table 4 Procedure of the MFNN with P-U learning algorithm

From: Matrix factorization with neural network for predicting circRNA-RBP interactions

Algorithm 2: The MFNN with P-U learning Algorithm
Input:Y: the known interaction matrix,  T: the times of sampling round
Set: Obtain set  P  and  U  from  Y, K: the size of  P  in each sampling round
Output:Fu: unlabeled sample score
Step 1: Initialize uU, t(u) ← 0, MFNN(u) ← 0
Step 2: For  t  from 1 to  T  do
 Randomly sample the set Ut  of size  K  in  U.
 Train a model  MFNNt to discriminate  P  against Ut
 For uU\Ut, update:
MFNN(u) ← MFNN(u) + MFNNt(u)
 t (u) ← t(u) + 1
end For
Step 3: Return  Fu = MFNN(u)/t(u)  for  uU