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http://dx.doi.org/10.5808/GI.2016.14.4.222

Metabolic Pathways Associated with Kimchi, a Traditional Korean Food, Based on In Silico Modeling of Published Data  

Shin, Ga Hee (Data Science Center, Insilicogen, Inc.)
Kang, Byeong-Chul (Data Science Center, Insilicogen, Inc.)
Jang, Dai Ja (Processing Technology Research Group, Korea Food Research Institute)
Abstract
Kimchi is a traditional Korean food prepared by fermenting vegetables, such as Chinese cabbage and radishes, which are seasoned with various ingredients, including red pepper powder, garlic, ginger, green onion, fermented seafood (Jeotgal), and salt. The various unique microorganisms and bioactive components in kimchi show antioxidant activity and have been associated with an enhanced immune response, as well as anti-cancer and anti-diabetic effects. Red pepper inhibits decay due to microorganisms and prevents food from spoiling. The vast amount of biological information generated by academic and industrial research groups is reflected in a rapidly growing body of scientific literature and expanding data resources. However, the genome, biological pathway, and related disease data are insufficient to explain the health benefits of kimchi because of the varied and heterogeneous data types. Therefore, we have constructed an appropriate semantic data model based on an integrated food knowledge database and analyzed the functional and biological processes associated with kimchi in silico. This complex semantic network of several entities and connections was generalized to answer complex questions, and we demonstrated how specific disease pathways are related to kimchi consumption.
Keywords
computer simulation; metabolism; semantic modeling; traditional Korean food;
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