Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data
Copyright © 2023 Elsevier B.V. All rights reserved..
Drug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to simplify the drug discovery process, which in turn reduces the costs and risks that are associated with de novo development. Representing biomedical data in the form of a graph is a simple and effective method to depict the underlying structure of the information. Using deep neural networks in combination with this data represents a promising approach to address drug repurposing. This paper presents BEHOR a more comprehensive version of the REDIRECTION model, which was previously presented. Both versions utilize the DISNET biomedical graph as the primary source of information, providing the model with extensive and intricate data to tackle the drug repurposing challenge. This new version's results for the reported metrics in the RepoDB test are 0.9604 for AUROC and 0.9518 for AUPRC. Additionally, a discussion is provided regarding some of the novel predictions to demonstrate the reliability of the model. The authors believe that BEHOR holds promise for generating drug repurposing hypotheses and could greatly benefit the field.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:145 |
|---|---|
| Enthalten in: |
Artificial intelligence in medicine - 145(2023) vom: 15. Nov., Seite 102687 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ayuso-Muñoz, Adrián [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
DISNET knowledge base |
|---|
| Anmerkungen: |
Date Completed 06.11.2023 Date Revised 10.11.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.artmed.2023.102687 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM364170662 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM364170662 | ||
| 003 | DE-627 | ||
| 005 | 20231226094857.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.artmed.2023.102687 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1213.xml |
| 035 | |a (DE-627)NLM364170662 | ||
| 035 | |a (NLM)37925215 | ||
| 035 | |a (PII)S0933-3657(23)00201-4 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ayuso-Muñoz, Adrián |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 06.11.2023 | ||
| 500 | |a Date Revised 10.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Elsevier B.V. All rights reserved. | ||
| 520 | |a Drug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to simplify the drug discovery process, which in turn reduces the costs and risks that are associated with de novo development. Representing biomedical data in the form of a graph is a simple and effective method to depict the underlying structure of the information. Using deep neural networks in combination with this data represents a promising approach to address drug repurposing. This paper presents BEHOR a more comprehensive version of the REDIRECTION model, which was previously presented. Both versions utilize the DISNET biomedical graph as the primary source of information, providing the model with extensive and intricate data to tackle the drug repurposing challenge. This new version's results for the reported metrics in the RepoDB test are 0.9604 for AUROC and 0.9518 for AUPRC. Additionally, a discussion is provided regarding some of the novel predictions to demonstrate the reliability of the model. The authors believe that BEHOR holds promise for generating drug repurposing hypotheses and could greatly benefit the field | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a DISNET knowledge base | |
| 650 | 4 | |a Drug repositioning | |
| 650 | 4 | |a Drug repurposing | |
| 650 | 4 | |a Graph deep learning (GDL) | |
| 650 | 4 | |a Graph neural networks (GNN) | |
| 700 | 1 | |a Prieto-Santamaría, Lucía |e verfasserin |4 aut | |
| 700 | 1 | |a Ugarte-Carro, Esther |e verfasserin |4 aut | |
| 700 | 1 | |a Serrano, Emilio |e verfasserin |4 aut | |
| 700 | 1 | |a Rodríguez-González, Alejandro |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Artificial intelligence in medicine |d 1995 |g 145(2023) vom: 15. Nov., Seite 102687 |w (DE-627)NLM075578344 |x 1873-2860 |7 nnns |
| 773 | 1 | 8 | |g volume:145 |g year:2023 |g day:15 |g month:11 |g pages:102687 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.artmed.2023.102687 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 145 |j 2023 |b 15 |c 11 |h 102687 | ||