Predict multi-type drug–drug interactions in cold start scenario
Abstract Background Prediction of drug–drug interactions (DDIs) can reveal potential adverse pharmacological reactions between drugs in co-medication. Various methods have been proposed to address this issue. Most of them focus on the traditional link prediction between drugs, however, they ignore t...
Main Authors: | Zun Liu, Xing-Nan Wang, Hui Yu, Jian-Yu Shi, Wen-Min Dong |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2022-02-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-022-04610-4 |
Similar Items
-
Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects
by: Pieter Dewulf, et al.
Published: (2021-05-01) -
Data-driven prediction of adverse drug reactions induced by drug-drug interactions
by: Ruifeng Liu, et al.
Published: (2017-06-01) -
TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs
by: Jian-Yu Shi, et al.
Published: (2018-11-01) -
The Influence of Pharmacogenetics on the Clinical Relevance of Pharmacokinetic Drug–Drug Interactions: Drug–Gene, Drug–Gene–Gene and Drug–Drug–Gene Interactions
by: Martina Hahn, et al.
Published: (2021-05-01) -
Drug–Drug–Gene Interactions in Cardiovascular Medicine
by: Asiimwe IG, et al.
Published: (2022-11-01)