Detecting drug communities and predicting comprehensive drug–drug interactions via balance regularized semi-nonnegative matrix factorization
Abstract Background Because drug–drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approa...
Main Authors: | Jian-Yu Shi, Kui-Tao Mao, Hui Yu, Siu-Ming Yiu |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2019-04-01
|
Series: | Journal of Cheminformatics |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13321-019-0352-9 |
Similar Items
-
Discriminative and Graph Regularized Nonnegative Matrix Factorization with Kernel Method
by: LI Xiangli, ZHANG Ying
Published: (2020-11-01) -
Nonnegative Matrix Factorization with Joint Regularization of Manifold Learning and Pairwise Constraints
by: CAO Jiawei, QIAN Pengjiang
Published: (2020-07-01) -
Multi-View Image Clustering via Representations Fusion Method With Semi-Nonnegative Matrix Factorization
by: Guopeng Li, et al.
Published: (2021-01-01) -
Multiple Partial Regularized Nonnegative Matrix Factorization for Predicting Ontological Functions of lncRNAs
by: Jianbang Zhao, et al.
Published: (2019-01-01) -
Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation
by: Lin Zhang, et al.
Published: (2019-01-01)