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A Quadruple Revolution: Deciphering Biological Complexity with Artificial Intelligence, Multiomics, Precision Medicine, and Planetary Health.
Cong, Yi; Endo, Toshinori.
Afiliación
  • Cong Y; Information Biology Laboratory, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.
  • Endo T; Information Biology Laboratory, Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan.
OMICS ; 28(6): 257-260, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38813661
ABSTRACT
A quiet quadruple revolution has been in the making in systems science with convergence of (1) artificial intelligence, machine learning, and other digital technologies; (2) multiomics big data integration; (3) growing interest in the "variability science" of precision/personalized medicine that aims to account for patient-to-patient and between-population differences in disease susceptibilities and responses to health interventions such as drugs, nutrition, vaccines, and radiation; and (4) planetary health scholarship that both scales up and integrates biological, clinical, and ecological contexts of health and disease. Against this overarching background, this article presents and highlights some of the salient challenges and prospects of multiomics research, emphasizing the attendant pivotal role of systems medicine and systems biology. In addition, we emphasize the rapidly growing importance of planetary health research for systems medicine, particularly amid climate emergency, ecological degradation, and loss of planetary biodiversity. Looking ahead, we anticipate that the integration and utilization of multiomics big data and artificial intelligence will drive further progress in systems medicine and systems biology, heralding a promising future for both human and planetary health.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Inteligencia Artificial / Medicina de Precisión Límite: Humans Idioma: En Revista: OMICS Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: Japón

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Inteligencia Artificial / Medicina de Precisión Límite: Humans Idioma: En Revista: OMICS Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: Japón