Background: Traditional drug identification methods follow the "one drug-one target" thought. But those methods ignore the natural characters of human diseases. To overcome this limitation, many identification methods of drug-pathway association pairs have been developed, such as the integrative penalized matrix decomposition (iPaD) method. The iPaD method imposes the L1-norm penalty on the regularization term. However, lasso-type penalties have an obvious disadvantage, that is, the sparsity produced by them is too dispersive.
Results: Therefore, to improve the performance of the iPaD method, we propose a novel method named L2,1-iPaD to identify paired drug-pathway associations. In the L2,1-iPaD model, we use the L2,1-norm penalty to replace the L1-norm penalty since the L2,1-norm penalty can produce row sparsity.
Conclusions: By applying the L2,1-iPaD method to the CCLE and NCI-60 datasets, we demonstrate that the performance of L2,1-iPaD method is superior to existing methods. And the proposed method can achieve better enrichment in terms of discovering validated drug-pathway association pairs than the iPaD method by performing permutation test. The results on the two real datasets prove that our method is effective.
Keywords: Drug discovery; Integrative penalized matrix decomposition; L2,1-norm penalty; Sparse method.