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Transplanting network pharmacology technology into food science research: A comprehensive review on uncovering food-sourced functional factors and their health benefits.

Qing ShenLijun GeWeibo LuHuixiang WuLi ZhangJun XuOushan TangMuhammad ImranJing ZhengYeshun WuSi-Wei WangXi-Xi ZengJing XueKeyun Cheng
Published in: Comprehensive reviews in food science and food safety (2024)
Network pharmacology is an emerging interdisciplinary research method. The application of network pharmacology to reveal the nutritional effects and mechanisms of active ingredients in food is of great significance in promoting the development of functional food, facilitating personalized nutrition, and exploring the mechanisms of food health effects. This article systematically reviews the application of network pharmacology in the field of food science using a literature review method. The application progress of network pharmacology in food science is discussed, and the mechanisms of functional factors in food on the basis of network pharmacology are explored. Additionally, the limitations and challenges of network pharmacology are discussed, and future directions and application prospects are proposed. Network pharmacology serves as an important tool to reveal the mechanisms of action and health benefits of functional factors in food. It helps to conduct in-depth research on the biological activities of individual ingredients, composite foods, and compounds in food, and assessment of the potential health effects of food components. Moreover, it can help to control and enhance their functionality through relevant information during the production and processing of samples to guarantee food safety. The application of network pharmacology in exploring the mechanisms of functional factors in food is further analyzed and summarized. Combining machine learning, artificial intelligence, clinical experiments, and in vitro validation, the achievement transformation of functional factor in food driven by network pharmacology is of great significance for the future development of network pharmacology research.
Keyphrases
  • human health
  • machine learning
  • artificial intelligence
  • public health
  • healthcare
  • risk assessment
  • systematic review
  • big data
  • health information
  • dna methylation
  • network analysis
  • case report
  • current status