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HGTDR: Advancing drug repurposing with heterogeneous graph transformers.
Gharizadeh, Ali; Abbasi, Karim; Ghareyazi, Amin; Mofrad, Mohammad R K; Rabiee, Hamid R.
Afiliación
  • Gharizadeh A; Department of Computer Engineering, Sharif University of Technology, Tehran, P.O. Box 11155-9517, Iran.
  • Abbasi K; Department of Computer Engineering, Sharif University of Technology, Tehran, P.O. Box 11155-9517, Iran.
  • Ghareyazi A; Department of Computer Engineering, Sharif University of Technology, Tehran, P.O. Box 11155-9517, Iran.
  • Mofrad MRK; Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, P.O. Box 94720-1740, United States.
  • Rabiee HR; Department of Computer Engineering, Sharif University of Technology, Tehran, P.O. Box 11155-9517, Iran.
Bioinformatics ; 40(7)2024 07 01.
Article en En | MEDLINE | ID: mdl-38913860
ABSTRACT
MOTIVATION Drug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still need to meet expectations. Therefore, it is crucial to offer a systematic approach for drug repurposing to achieve cost savings and enhance human lives. In recent years, using biological network-based methods for drug repurposing has generated promising results. Nevertheless, these methods have limitations. Primarily, the scope of these methods is generally limited concerning the size and variety of data they can effectively handle. Another issue arises from the treatment of heterogeneous data, which needs to be addressed or converted into homogeneous data, leading to a loss of information. A significant drawback is that most of these approaches lack end-to-end functionality, necessitating manual implementation and expert knowledge in certain stages.

RESULTS:

We propose a new solution, Heterogeneous Graph Transformer for Drug Repurposing (HGTDR), to address the challenges associated with drug repurposing. HGTDR is a three-step approach for knowledge graph-based drug repurposing (1) constructing a heterogeneous knowledge graph, (2) utilizing a heterogeneous graph transformer network, and (3) computing relationship scores using a fully connected network. By leveraging HGTDR, users gain the ability to manipulate input graphs, extract information from diverse entities, and obtain their desired output. In the evaluation step, we demonstrate that HGTDR performs comparably to previous methods. Furthermore, we review medical studies to validate our method's top 10 drug repurposing suggestions, which have exhibited promising results. We also demonstrated HGTDR's capability to predict other types of relations through numerical and experimental validation, such as drug-protein and disease-protein inter-relations. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https//github.com/bcb-sut/HGTDR and http//git.dml.ir/BCB/HGTDR.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reposicionamiento de Medicamentos Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reposicionamiento de Medicamentos Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido