Systematic network analysis of herb formula in Traditional East Asian Medicine discloses synergistic operation of medicinal herb pairs with statistical significance
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Traditional East Asian Medicine (TEAM) prescriptions typically consist of several herbs based on the assumption that the herbs operate synergistically and/or cooperate on several related pathways simultaneously. This is a general concept that is widely accepted in TEAM, but it has not been tested systematically. To check this assumption statistically, we have text mined traditional Korean medicine text the Inje-ji(仁濟志, Collections of benevolent savings), a text that contains more than 5000 herb-cocktail prescriptions. We created herb–pairing network based on herb–herb pairing specificity and performed a systematic network analysis. Herbs were shown to be used selectively with other herbs and not randomly. Moreover, herb pairs were more specifically associated with symptoms than were single herbs. Single herbs and combinations of herbs specifically used for diabetes mellitus were successfully identified. As conclusion, herb-pairings in TEAM are not randomly constructed; instead, each herb was selectively used with other herbs. In terms of statistical significance, herb pairs were more specifically associated with symptoms than were single herbs alone. Collectively, these results suggest that it may be important to understand the interactions among multiple ingredients contained in herb pairs rather than trying to identify a single compound to resolve symptoms.
Abstract
Traditional East Asian Medicine (TEAM) prescriptions typically consist of several herbs based on the assumption that the herbs operate synergistically and/or cooperate on several related pathways simultaneously. This is a general concept that is widely accepted in TEAM, but it has not been tested systematically. To check this assumption statistically, we have text mined traditional Korean medicine text the Inje-ji(仁濟志, Collections of benevolent savings), a text that contains more than 5000 herb-cocktail prescriptions. We created herb–pairing network based on herb–herb pairing specificity and performed a systematic network analysis. Herbs were shown to be used selectively with other herbs and not randomly. Moreover, herb pairs were more specifically associated with symptoms than were single herbs. Single herbs and combinations of herbs specifically used for diabetes mellitus were successfully identified. As conclusion, herb-pairings in TEAM are not randomly constructed; instead, each herb was selectively used with other herbs. In terms of statistical significance, herb pairs were more specifically associated with symptoms than were single herbs alone. Collectively, these results suggest that it may be important to understand the interactions among multiple ingredients contained in herb pairs rather than trying to identify a single compound to resolve symptoms.
참고문헌 (9)
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