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Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method.

Abstract：

:Identifying the unintended effects of drugs (side effects) is a very important issue in pharmacological studies. The laboratory verification of associations between drugs and side effects requires costly, time-intensive research. Thus, an approach to predicting drug side effects based on known side effects, using a computational model, is highly desirable. To provide such a model, we used openly available data resources to model drugs and side effects as a bipartite graph. The drug-drug network is constructed using the word2vec model where the edges between drugs represent the semantic similarity between them. We integrated the bipartite graph and the semantic similarity graph using a matrix factorization method and a diffusion based model. Our results show the effectiveness of this integration by computing weighted (i.e., ranked) predictions of initially unknown links between side effects and drugs.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Timilsina M,Tandan M,d'Aquin M,Yang H

doi

10.1038/s41598-019-46939-6

subject

Has Abstract

pub_date

2019-07-18 00:00:00

pages

10436

issue

1

issn

2045-2322

pii

10.1038/s41598-019-46939-6

journal_volume

9

pub_type

杂志文章

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