转化式学习
农业
生物
心理学
发展心理学
生态学
作者
Albert-Ĺaszló Barabási,Giulia Menichetti,Joseph Loscalzo
出处
期刊:Nature food
[Nature Portfolio]
日期:2019-12-09
卷期号:1 (1): 33-37
被引量:284
标识
DOI:10.1038/s43016-019-0005-1
摘要
Our understanding of how diet affects health is limited to 150 key nutritional components that are tracked and catalogued by the United States Department of Agriculture and other national databases. Although this knowledge has been transformative for health sciences, helping unveil the role of calories, sugar, fat, vitamins and other nutritional factors in the emergence of common diseases, these nutritional components represent only a small fraction of the more than 26,000 distinct, definable biochemicals present in our food—many of which have documented effects on health but remain unquantified in any systematic fashion across different individual foods. Using new advances such as machine learning, a high-resolution library of these biochemicals could enable the systematic study of the full biochemical spectrum of our diets, opening new avenues for understanding the composition of what we eat, and how it affects health and disease. Advances such as machine learning may enable the full biochemical spectrum of food to be studied systematically. Uncovering the ‘dark matter’ of nutrition could open new avenues for a greater understanding of the composition of what we eat and how it relates to health and disease
科研通智能强力驱动
Strongly Powered by AbleSci AI