TY - JOUR T1 - FoodMine: Exploring Food Contents in Scientific Literature JF - bioRxiv DO - 10.1101/2019.12.17.880062 SP - 2019.12.17.880062 AU - Forrest Hooton AU - Giulia Menichetti AU - Albert-László Barabási Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/12/18/2019.12.17.880062.abstract N2 - 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 188 nutritional components that are essential for our health, a tiny fraction of the total number of chemicals present in our food. 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. ER -