DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions
Abstract Background Drug-drug interactions (DDIs) are a major concern in patients'
medication. It's unfeasible to identify all potential DDIs using experimental methods which …
medication. It's unfeasible to identify all potential DDIs using experimental methods which …
Positive-unlabeled learning for inferring drug interactions based on heterogeneous attributes
Background Investigating and understanding drug-drug interactions (DDIs) is important in
improving the effectiveness of clinical care. DDIs can occur when two or more drugs are …
improving the effectiveness of clinical care. DDIs can occur when two or more drugs are …
Predicting drug–target interaction using positive-unlabeled learning
Identifying interactions between drug compounds and target proteins is an important
process in drug discovery. It is time-consuming and expensive to determine interactions …
process in drug discovery. It is time-consuming and expensive to determine interactions …
Predicting drug-drug interactions based on integrated similarity and semi-supervised learning
C Yan, G Duan, Y Zhang, FX Wu… - … /ACM transactions on …, 2020 - ieeexplore.ieee.org
A drug-drug interaction (DDI) is defined as an association between two drugs where the
pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually …
pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually …
Drug-target interaction prediction: end-to-end deep learning approach
NRC Monteiro, B Ribeiro… - IEEE/ACM transactions on …, 2020 - ieeexplore.ieee.org
The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug
discovery and repositioning process, as the effectiveness of the currently available antibiotic …
discovery and repositioning process, as the effectiveness of the currently available antibiotic …
Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities
NN Wang, B Zhu, XL Li, S Liu, JY Shi… - Journal of Chemical …, 2023 - ACS Publications
Detecting drug–drug interactions (DDIs) is an essential step in drug development and drug
administration. Given the shortcomings of current experimental methods, the machine …
administration. Given the shortcomings of current experimental methods, the machine …
Prediction of drug–target interactions based on network representation learning and ensemble learning
Identifying interactions between drugs and target proteins is a critical step in the drug
development process, as it helps identify new targets for drugs and accelerate drug …
development process, as it helps identify new targets for drugs and accelerate drug …
DSN-DDI: an accurate and generalized framework for drug–drug interaction prediction by dual-view representation learning
Drug–drug interaction (DDI) prediction identifies interactions of drug combinations in which
the adverse side effects caused by the physicochemical incompatibility have attracted much …
the adverse side effects caused by the physicochemical incompatibility have attracted much …
[HTML][HTML] A meta-learning framework using representation learning to predict drug-drug interaction
SS Deepika, TV Geetha - Journal of biomedical informatics, 2018 - Elsevier
Abstract Motivation Predicting Drug-Drug Interaction (DDI) has become a crucial step in the
drug discovery and development process, owing to the rise in the number of drugs co …
drug discovery and development process, owing to the rise in the number of drugs co …
Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning
Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has
aroused widespread concern in academia and industry. The existing computational DDI …
aroused widespread concern in academia and industry. The existing computational DDI …