Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space

Algorithm 1

Algorithm for learning the matrix factorization model.

Input: Known drug-disease association set  , learning rate  , regularization coefficient  

Output: Drug feature vectors , disease feature vectors

,  

repeat

 for training sample    do

 end

until the shrinkage of training error is below a user-defined value