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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Frontiers in Pharmacology

ISSN

1663-9812

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85186419602