Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions.

ABSTRACT: Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has been carried out to predict ADRs in the early phases of drug development, with the goal of reducing possible future risks. The pre-clinical and clinical phases of drug research can be time-consuming and cost-ineffective, so academics are looking forward to more extensive data mining and machine learning methods to be applied in this field of study. In this paper, we try to construct a drug-to-drug network based on non-clinical data sources. The network presents underlying relationships between drug pairs according to their common ADRs. Then, multiple node-l

SUBMITTER: Zhou F 

PROVIDER: S-EPMC9957267 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature