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SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations.
Tang, Yi-Ching; Li, Rongbin; Tang, Jing; Zheng, W Jim; Jiang, Xiaoqian.
Afiliación
  • Tang YC; Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States.
  • Li R; Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States.
  • Tang J; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
  • Zheng WJ; Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States.
  • Jiang X; Center for Safe Artificial Intelligence for Healthcare, McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, United States.
Res Sq ; 2024 Apr 30.
Article en En | MEDLINE | ID: mdl-38746131
ABSTRACT

Background:

The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and context-dependent networks. This limitation constrains the applicability of current methods.

Results:

We introduced SAFER, a Sub-hypergraph Attention-based graph model, addressing these issues by incorporating complex relationships among biological knowledge networks and considering dosing effects on subject-specific networks. SAFER outperformed previous models on the benchmark and the independent test set. The analysis of subgraph attention weight for the lung cancer cell line highlighted JAK-STAT signaling pathway, PRDM12, ZNF781, and CDC5L that have been implicated in lung fibrosis.

Conclusions:

SAFER presents an interpretable framework designed to identify drug-responsive signals. Tailored for comprehending dose effects on subject-specific molecular contexts, our model uniquely captures dose-level drug combination responses. This capability unlocks previously inaccessible avenues of investigation compared to earlier models. Finally, the SAFER framework can be leveraged by future inquiries to investigate molecular networks that uniquely characterize individual patients.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Res Sq Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Res Sq Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos