Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy.

ABSTRACT: Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed for accessing high-quality negative samples. Such strategy applied the random walk with restart (RWR) algorithm on a chemical-chemical interaction network to select pairs of drugs and side effects, s

SUBMITTER: Liang H 

PROVIDER: S-EPMC7232712 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature