Dataset (Main folder).zip
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Supporting Data for “The curative potential of herbal medicine on diabetes and its complication: a pharmacological study with machine learning intelligence”
A pattern or syndrome in response to a multicomponent system is the actual target of herbal medicine treatment. However, it is a substantial challenge to fill the gap between a contributive compound profile in herbal medicine (especially a formula) and its biological features. This study aims to establish a feasible component-mining strategy, which provides a strong prediction of key compounds in support of experimental and clinical observations. Given interdisciplinary scope of life science and mathematical statistics, the relationship between chemical profile and bioactivities is measured by a model termed mathematical prediction bioactivity, in which gray relational analysis, multiple linear/non-linear regression analysis (including t-distributed stochastic neighbor embedding), and radial basis function analysis are involved. R language programming-dependent analysis is adopted with add-on packages, including UniDOE, Factoextra, FactoMineR, Factanal, Rtsne, and Nnet. By using this assessment method in a biological experiment, it is identified that 6-shogaol extracted from Ginger-Coptis formula (a herbal formula) is beneficial for diabetic retinopathy (DR) treatment. The study provides both a novel compound 6-shogalol for DR treatment and a new strategy for mining key contributors in a multicomponent system.