A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects.

ABSTRACT:

Motivation

A critical issue in drug benefit-risk assessment is to determine the frequency of side effects, which is performed by randomized controlled trails. Computationally predicted frequencies of drug side effects can be used to effectively guide the randomized controlled trails. However, it is more challenging to predict drug side effect frequencies, and thus only a few studies cope with this problem.

Results

In this work, we propose a neighborhood-regularization method (NRFSE) that leverages multiview data on drugs and side effects to predict the frequency of side effects. First, we adopt a class-weighted non-negative matrix factorization to decompose the drug-side effect frequency matrix, in which Gaussian likelihood is used to model unknown drug-side effect pairs. Se

SUBMITTER: Wang L 

PROVIDER: S-EPMC10491955 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature