TY - GEN
T1 - FooDisNET
T2 - 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
AU - Lin, Chu Yun
AU - Lee, Jung Yu
AU - Huang, Sing Han
AU - Hsu, Yen Chao
AU - Hsu, Nung Yu
AU - Yang, Jinn Moon
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Natural compounds and nutrients in plants are beneficial to human health and reduce the risk of diseases. These compounds can directly or indirectly act on specific proteins to regulate biochemical pathways and affect disease progression or prevent the occurrence of chronic diseases. Several databases provide information about the ingredients of various plants, Chinese medicine, and the plant-target associations. However, there is still a lack of a database linking food, compounds, proteins, and diseases. Here, we propose a FooDisNET database to provide food-compound-protein-disorder connections. FooDisNET contains 6,329 foods, 53,920 compounds, and 22,865 target proteins, of which 4,092 target proteins are associated with 18,689 disorders. Based on our scoring strategy, we constructed a user-friendly website that enables the user to query the associations from four perspectives, namely foods, compounds, proteins, and disorders. The score reflects the prevalence of foods, the rarity of compounds, the specificity of proteins, and the universality of disorders to describe food-compound-protein-disorder interaction networks. We believe that the FooDisNET database not only assists general uses to investigate food ingredients that are beneficial to their health but also helps the analysis of the role of natural compounds in the drug discovery and development process.
AB - Natural compounds and nutrients in plants are beneficial to human health and reduce the risk of diseases. These compounds can directly or indirectly act on specific proteins to regulate biochemical pathways and affect disease progression or prevent the occurrence of chronic diseases. Several databases provide information about the ingredients of various plants, Chinese medicine, and the plant-target associations. However, there is still a lack of a database linking food, compounds, proteins, and diseases. Here, we propose a FooDisNET database to provide food-compound-protein-disorder connections. FooDisNET contains 6,329 foods, 53,920 compounds, and 22,865 target proteins, of which 4,092 target proteins are associated with 18,689 disorders. Based on our scoring strategy, we constructed a user-friendly website that enables the user to query the associations from four perspectives, namely foods, compounds, proteins, and disorders. The score reflects the prevalence of foods, the rarity of compounds, the specificity of proteins, and the universality of disorders to describe food-compound-protein-disorder interaction networks. We believe that the FooDisNET database not only assists general uses to investigate food ingredients that are beneficial to their health but also helps the analysis of the role of natural compounds in the drug discovery and development process.
KW - diet and health
KW - disease prevention
KW - drug development
KW - food-disease interaction network
KW - natural products
KW - phytochemicals
KW - query performance
UR - http://www.scopus.com/inward/record.url?scp=85099600904&partnerID=8YFLogxK
U2 - 10.1109/BIBE50027.2020.00039
DO - 10.1109/BIBE50027.2020.00039
M3 - Conference contribution
AN - SCOPUS:85099600904
T3 - Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
SP - 190
EP - 195
BT - Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 October 2020 through 28 October 2020
ER -
