Paper Info
Title
Identifying drug-pathway association pairs based on L2, 1-integrative penalized matrix decomposition.
Abstract
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,L-1-iPaD to identify paired drug-pathway associations. In the L-2,L-1-iPaD model, we use the L-2,L-1-norm penalty to replace the L-1-norm penalty since the L-2,L-1-norm penalty can produce row sparsity. Conclusions: By applying the L-2,L-1-iPaD method to the CCLE and NCI-60 datasets, we demonstrate that the performance of L-2,L-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.
Year
DOI
Venue
2017
10.1186/s12918-017-0480-7
BMC SYSTEMS BIOLOGY
Keywords
Field
DocType
Drug discovery,Sparse method,Integrative penalized matrix decomposition,L-2,L-1-norm penalty
Biology,Matrix decomposition,Systems biology,Regularization (mathematics),Artificial intelligence,Bioinformatics,Resampling,Drug Pathway,Machine learning
Journal
Volume
Issue
ISSN
11
SUPnan
1752-0509
Citations 
PageRank 
References 
0
0.34
20
Authors
6
Name
Order
Citations
PageRank
Liu Jin-Xing14016.11
Dong-Qin Wang200.34
Chun-hou Zheng373271.79
Gao Ying-Lian42918.73
Sha-sha Wu501.01
Junliang Shang64214.78