Publicação
Complex graph neural networks for medication interaction verification
| Resumo: | This paper presents the development and application of graph neural networks to verify drug interactions, consisting of drug-protein networks. For this, the DrugBank databases were used, creating four complex networks of interactions: target proteins, transport proteins, carrier proteins, and enzymes. The Louvain and Girvan-Newman community detection algorithms were used to establish communities and validate the interactions between them. Positive results were obtained when checking the interactions of two sets of drugs for disease treatments: diabetes and anxiety; diabetes and antibiotics. There were found 371 interactions by the Girvan-Newman algorithm and 58 interactions via Louvain. |
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| Autores principais: | Westarb, Gustavo |
| Outros Autores: | Stefenon, Stefano Frizzo; Hoppe, Aurélio Faustino; Sartori, Andreza; Klaar, Anne Carolina Rodrigues; Leithardt, Valderi Reis Quietinho |
| Assunto: | drug interaction graph neural network communities detection |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Politécnica de Portalegre |
| Idioma: | inglês |
| Origem: | Universidade Politécnica de Portalegre |
| Resumo: | This paper presents the development and application of graph neural networks to verify drug interactions, consisting of drug-protein networks. For this, the DrugBank databases were used, creating four complex networks of interactions: target proteins, transport proteins, carrier proteins, and enzymes. The Louvain and Girvan-Newman community detection algorithms were used to establish communities and validate the interactions between them. Positive results were obtained when checking the interactions of two sets of drugs for disease treatments: diabetes and anxiety; diabetes and antibiotics. There were found 371 interactions by the Girvan-Newman algorithm and 58 interactions via Louvain. |
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