Prediction of multiple types of drug interactions based on multi-scale fusion and dual-view fusion

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AT2VW2EZU" target="_blank" >RIV/00216208:11320/25:T2VW2EZU - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186419602&doi=10.3389%2ffphar.2024.1354540&partnerID=40&md5=9228680c112d426e47b3fd7b5b671c59" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186419602&doi=10.3389%2ffphar.2024.1354540&partnerID=40&md5=9228680c112d426e47b3fd7b5b671c59</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fphar.2024.1354540" target="_blank" >10.3389/fphar.2024.1354540</a>

Result language

angličtina

Original language name

Prediction of multiple types of drug interactions based on multi-scale fusion and dual-view fusion

Original language description

Potential drug-drug interactions (DDI) can lead to adverse drug reactions (ADR), and DDI prediction can help pharmacy researchers detect harmful DDI early. However, existing DDI prediction methods fall short in fully capturing drug information. They typically employ a single-view input, focusing solely on drug features or drug networks. Moreover, they rely exclusively on the final model layer for predictions, overlooking the nuanced information present across various network layers. To address these limitations, we propose a multi-scale dual-view fusion (MSDF) method for DDI prediction. More specifically, MSDF first constructs two views, topological and feature views of drugs, as model inputs. Then a graph convolutional neural network is used to extract the feature representations from each view. On top of that, a multi-scale fusion module integrates information across different graph convolutional layers to create comprehensive drug embeddings. The embeddings from the two views are summed as the final representation for classification. Experiments on two real-world datasets demonstrate that MSDF achieves higher accuracy than state-of-the-art methods, as the dual-view, multi-scale approach better captures drug characteristics. Copyright © 2024 Pan, Lu, Wu, Kang, Huang, Lin and Yang.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Pharmacology

ISSN

1663-9812

e-ISSN

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Volume of the periodical

15

Issue of the periodical within the volume

2024

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

1-16

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85186419602