Exploring food contents in scientific literature with FoodMine.

Exploring food contents in scientific literature with FoodMine.
复制标题

DOI:
10.1038/s41598-020-73105-0
复制
发表时间:
2020-10-01
期刊:
影响因子:
4.6
通讯作者:
Barabási AL
Barabási AL
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Hooton F;Menichetti G;Barabási AL

文献摘要

参考文献

被引文献

相似文献

由于它所携带的许多化学和营养成分,饮食对人体健康有着至关重要的影响。然而,目前可获得的关于食物成分的综合数据库只涵盖了我们食物中存在的化学物质总数的一小部分,主要关注对我们健康至关重要的营养成分。事实上,成千上万的其他分子,其中许多对健康有很好的影响,仍然没有被追踪到。为了探索关于食物成分的知识体系,我们建立了FoodMine,这是一种算法,它使用自然语言处理来识别PubMed中可能报告大蒜和可可化学成分的论文。从每篇报告的化学物质数量中提取信息后,我们发现科学文献中包含了大量关于食品中详细化学成分的信息,而这些信息目前还没有整合到数据库中。最后,我们使用无监督机器学习来创建化学嵌入,发现FoodMine识别的化学物质往往与健康直接相关,这反映了科学界对食物中与健康相关的化学物质的关注。
Thanks to the many chemical and nutritional components it carries, diet critically affects human health. However, the currently available comprehensive databases on food composition cover only a tiny fraction of the total number of chemicals present in our food, focusing on the nutritional components essential for our health. Indeed, thousands of other molecules, many of which have well documented health implications, remain untracked. To explore the body of knowledge available on food composition, we built FoodMine, an algorithm that uses natural language processing to identify papers from PubMed that potentially report on the chemical composition of garlic and cocoa. After extracting from each paper information on the reported quantities of chemicals, we find that the scientific literature carries extensive information on the detailed chemical components of food that is currently not integrated in databases. Finally, we use unsupervised machine learning to create chemical embeddings, finding that the chemicals identified by FoodMine tend to have direct health relevance, reflecting the scientific community’s focus on health-related chemicals in our food.
DOI: 10.1016/j.toxrep.2017.03.001
发表时间: 2017-01-01
期刊: TOXICOLOGY REPORTS
影响因子: --
作者:
Oyekunle, J. A. O.;Akindolani, O. A.;Adekunle, A. S.
通讯作者: Adekunle, A. S.
DOI: 10.1002/mnfr.200900580
发表时间: 2010-11-01
影响因子: 5.2
作者:
Arranz, Sara;Manuel Silvan, Jose;Saura-Calixto, Fulgencio
通讯作者: Saura-Calixto, Fulgencio
DOI: 10.1158/1940-6207.capr-14-0172
发表时间: 2015-03
期刊: Cancer prevention research (Philadelphia, Pa.)
影响因子: --
作者:
Nicastro HL;Ross SA;Milner JA
通讯作者: Milner JA
DOI: 10.1093/nar/gky868
发表时间: 2019-01-08
影响因子: 14.9
作者:
Davis AP;Grondin CJ;Johnson RJ;Sciaky D;McMorran R;Wiegers J;Wiegers TC;Mattingly CJ
通讯作者: Mattingly CJ