Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization

Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization

Abstract Background Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abi...

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Bibliographic Details
Main Authors: Hui Yu, Kui-Tao Mao, Jian-Yu Shi, Hua Huang, Zhi Chen, Kai Dong, Siu-Ming Yiu
Format: Article
Language:English
Published: BioMed Central 2018-04-01
Series:BMC Systems Biology
Subjects:
Drug-drug interaction
Nonnegative matrix factorization
Regression
Network community
Balance theory
Online Access:http://link.springer.com/article/10.1186/s12918-018-0532-7

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http://link.springer.com/article/10.1186/s12918-018-0532-7

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