Identifying drug-pathway association pairs based on L(2,1)-integrative penalized matrix decomposition

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 L(1)-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 L(2,1)-iPaD to identify paired drug-pathway associations. In the L(2,1)-iPaD model, we use the L(2,1)-norm penalty to replace the L(1)-norm penalty since the L(2,1)-norm penalty can produce row sparsity. CONCLUSIONS: By applying the L(2,1)-iPaD method to the CCLE and NCI-60 datasets, we demonstrate that the performance of L(2,1)-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.